https://github.com/astrogilda/learning-deep-learning

Paper reading notes on Deep Learning and Machine Learning

https://github.com/astrogilda/learning-deep-learning

Repository

Paper reading notes on Deep Learning and Machine Learning

https://github.com/astrogilda/Learning-Deep-Learning/blob/master/

# Paper notes
This repository contains my paper reading notes on deep learning and machine learning. It is inspired by [Denny Britz](https://github.com/dennybritz/deeplearning-papernotes) and [Daniel Takeshi](https://github.com/DanielTakeshi/Paper_Notes).

**New year resolution for 2020: read at least three paper a week and a high a high quality github repo a month!**

## Summary of Topics
The summary of the papers read in 2019 can be found [here on Towards Data Science](https://towardsdatascience.com/the-200-deep-learning-papers-i-read-in-2019-7fb7034f05f7?source=friends_link&sk=7628c5be39f876b2c05e43c13d0b48a3).

The sections below records paper reading activity in chronological order. See notes organized according to subfields [here](organized.md) (up to 06-2019).

## Source of papers
Here is [a list of trustworthy sources of papers](trusty.md) in case I ran out of papers to read.

The list of resource in this [link](https://autonomous-driving.org/front/resources/) talks about various topics in Autonomous Driving.

## Github repos
- [simple-faster-rcnn-pytorch](https://github.com/chenyuntc/simple-faster-rcnn-pytorch/) (2.1k star) [[Notes](code_notes/simple_faster_rcnn.md)]
- [YOLACT/YOLACT++](https://github.com/dbolya/yolact) (2.1k star)
- [MonoLoco](https://github.com/vita-epfl/monoloco) (131 star)
- [A Baseline for 3D Multi-Object Tracking](https://github.com/xinshuoweng/AB3DMOT) (548 star)
- [ROLO: recurrent YOLO](https://github.com/Guanghan/ROLO)
- [point rend](https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend/point_rend)
- [Carla data export](https://github.com/Ozzyz/carla-data-export)
- [openpilot](https://github.com/commaai/openpilot/blob/devel/selfdrive/modeld/models/driving.cc)

## Youtube channels
- [Modern C++ for computer vision](https://www.youtube.com/playlist?list=PLgnQpQtFTOGR50iIOtO36nK6aNPtVq98C)
- [SLAM by Cyrill Stachniss](https://www.youtube.com/playlist?list=PLgnQpQtFTOGQrZ4O5QzbIHgl3b1JHimN_)


## 2020-04 (0)
- [SOLO: Segmenting Objects by Locations](https://arxiv.org/abs/1912.04488)
- [SOLOv2: Dynamic, Faster and Stronger](https://arxiv.org/abs/2003.10152)
- [EfficientDet: Scalable and Efficient Object Detection](https://arxiv.org/abs/1911.09070)
- [Improved Baselines with Momentum Contrastive Learning](https://arxiv.org/abs/2003.04297)

## 2020-03 (11)
- [VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition](https://arxiv.org/abs/1710.06288) [[Notes](paper_notes/vpgnet.md)] ICCV 2017
- [Which Tasks Should Be Learned Together in Multi-task Learning?](https://arxiv.org/abs/1905.07553) [[Notes](paper_notes/task_grouping.md)] [Stanford]
- [Multi-Task Learning as Multi-Objective Optimization](https://arxiv.org/abs/1810.04650) NeurIPS 2018
- [Taskonomy: Disentangling Task Transfer Learning](https://arxiv.org/abs/1804.08328) [[Notes](paper_notes/taskonomy.md)] CVPR 2018
- [Rethinking ImageNet Pre-training](https://arxiv.org/abs/1811.08883) [[Notes](paper_notes/rethinking_pretraining.md)] ICCV 2019 [Kaiming He]
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://arxiv.org/abs/1609.05158) CVPR 2016 [channel-to-pixel]
- [UnsuperPoint: End-to-end Unsupervised Interest Point Detector and Descriptor](https://arxiv.org/abs/1907.04011) [[Notes](paper_notes/unsuperpoint.md)] [superpoint]
- [KP2D: Neural Outlier Rejection for Self-Supervised Keypoint Learning](https://arxiv.org/abs/1912.10615) [[Notes](paper_notes/kp2d.md)] ICLR 2020 (pointNet)
- [KP3D: Self-Supervised 3D Keypoint Learning for Ego-motion Estimation](https://arxiv.org/abs/1912.03426) [[Notes](paper_notes/kp3d.md)] [Toyota, superpoint]
- [NG-RANSAC: Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses](https://arxiv.org/abs/1905.04132) [[Notes](paper_notes/ng_ransac.md)] ICCV 2019 [pointNet]
- [Learning to Find Good Correspondences](https://arxiv.org/abs/1711.05971) [[Notes](paper_notes/learning_correspondence.md)] CVPR 2018 Oral (pointNet)
- [Visual Odometry Revisited: What Should Be Learnt?](https://arxiv.org/abs/1909.09803) ICRA 2020
- [RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving](https://arxiv.org/abs/1911.09712) [[Notes](paper_notes/refined_mpl.md)] [Huawei, Mono3D]
- [DSP: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation](https://arxiv.org/abs/2002.01619) [[Notes](paper_notes/dsp.md)] AAAI 2020 (SenseTime, Mono3D)
- [Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks](https://arxiv.org/abs/1903.02193) (LLD, LSTM)
- [LaneNet: Towards End-to-End Lane Detection: an Instance Segmentation Approach](https://arxiv.org/abs/1802.05591) [[Notes](paper_notes/lanenet.md)] IV 2018 (LaneNet)
- [3D-LaneNet: End-to-End 3D Multiple Lane Detection](http://openaccess.thecvf.com/content_ICCV_2019/papers/Garnett_3D-LaneNet_End-to-End_3D_Multiple_Lane_Detection_ICCV_2019_paper.pdf) [[Notes](paper_notes/3d_lanenet.md)] ICCV 2019
- [Semi-Local 3D Lane Detection and Uncertainty Estimation](https://arxiv.org/abs/2003.05257) [[Notes](paper_notes/semilocal_3d_lanenet.md)] [GM Israel, 3D LLD]
- [Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection](https://arxiv.org/abs/2003.10656) (Apollo, 3D LLD)
- [Detecting Lane and Road Markings at A Distance with Perspective Transformer Layers](https://arxiv.org/abs/2003.08550)  (3D LLD)
- [Gliding vertex on the horizontal bounding box for multi-oriented object detection](https://arxiv.org/abs/1911.09358)
- [PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization](https://arxiv.org/abs/1505.07427) [[Notes](paper_notes/posenet.md)] ICCV 2015
- [PoseNet2: Modelling Uncertainty in Deep Learning for Camera Relocalization](https://arxiv.org/abs/1509.05909) ICRA 2016
- [PoseNet3: Geometric Loss Functions for Camera Pose Regression with Deep Learning](https://arxiv.org/abs/1704.00390) CVPR 2017
- [Learning Depth-Guided Convolutions for Monocular 3D Object Detection](https://arxiv.org/abs/1912.04799) (Mono3D)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/0801.pdf) CVPR 2018 spotlight (Stella Yu)
- [Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views](https://arxiv.org/abs/1505.05641) ICCV 2015 Oral
- [Towards High Performance Video Object Detection](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhu_Towards_High_Performance_CVPR_2018_paper.pdf) [[Notes](paper_notes/high_performance_video_od.md)] CVPR 2018
- [Towards High Performance Video Object Detection for Mobiles](https://arxiv.org/abs/1804.05830) [[Notes](paper_notes/high_performance_video_od_mobile.md)]
- [Theoretical insights into the optimization landscape of over-parameterized shallow neural networks](https://arxiv.org/abs/1707.04926) TIP 2018
- [The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning](https://arxiv.org/abs/1712.06559) ICML 2018
- [SGD on Neural Networks Learns Functions of Increasing Complexity](https://arxiv.org/abs/1905.11604) NIPS 2019 (SGD learns a linear classifier first)
- [Pay attention to the activations: a modular attention mechanism for fine-grained image recognition](https://arxiv.org/abs/1907.13075)
- [Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art](https://arxiv.org/abs/1704.05519v2) (latest update in Dec 2019)
- [Simultaneous Identification and Tracking of Multiple People Using Video and IMUs](http://openaccess.thecvf.com/content_CVPRW_2019/papers/BMTT/Henschel_Simultaneous_Identification_and_Tracking_of_Multiple_People_Using_Video_and_CVPRW_2019_paper.pdf) CVPR 2019
- [Detect-and-Track: Efficient Pose Estimation in Videos](http://openaccess.thecvf.com/content_cvpr_2018/papers/Girdhar_Detect-and-Track_Efficient_Pose_CVPR_2018_paper.pdf)
- [TrackNet: Simultaneous Object Detection and Tracking and Its Application in Traffic Video Analysis](https://arxiv.org/abs/1902.01466)
- [Video Action Transformer Network](https://arxiv.org/abs/1812.02707) CVPR 2019 oral
- [Online Real-time Multiple Spatiotemporal Action Localisation and Prediction](https://arxiv.org/abs/1611.08563) ICCV 2017
- [ ](https://zhuanlan.zhihu.com/p/65177442)
- [Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty](https://arxiv.org/abs/1711.09026) (on-board bbox prediction)
- [Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems](https://arxiv.org/abs/1809.07408) (Honda)
- [Unsupervised Traffic Accident Detection in First-Person Videos](https://arxiv.org/abs/1903.00618) IROS 2019 (Honda)
- [NEMO: Future Object Localization Using Noisy Ego Priors](https://arxiv.org/abs/1909.08150) (Honda)
- [Robust Aleatoric Modeling for Future Vehicle Localization](http://openaccess.thecvf.com/content_CVPRW_2019/papers/Precognition/Hudnell_Robust_Aleatoric_Modeling_for_Future_Vehicle_Localization_CVPRW_2019_paper.pdf) (perspective)
- [Multiple Object Forecasting: Predicting Future Object Locations in Diverse
Environments](https://arxiv.org/abs/1909.11944) WACV 2020 (perspective bbox, pedestrian)
- [LaneNet: Towards End-to-End Lane Detection: an Instance Segmentation Approach](https://arxiv.org/abs/1802.05591)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://arxiv.org/abs/1811.08383) ICCV 2019 (Song Han)
- [AGSS-VOS: Attention Guided Single-Shot Video Object Segmentation](http://openaccess.thecvf.com/content_ICCV_2019/papers/Lin_AGSS-VOS_Attention_Guided_Single-Shot_Video_Object_Segmentation_ICCV_2019_paper.pdf) ICCV 2019
- [One-Shot Video Object Segmentation](https://arxiv.org/abs/1611.05198) CVPR 2017
- [Looking Fast and Slow: Memory-Guided Mobile Video Object Detection](https://arxiv.org/abs/1903.10172) CVPR 2018
- [Real-Time Seamless Single Shot 6D Object Pose Prediction](https://arxiv.org/abs/1711.08848) CVPR 2018
- [Learn to Combine Modalities in Multimodal Deep Learning](https://arxiv.org/abs/1805.11730) (sensor fusion, general DL)
- [PointRNN: Point Recurrent Neural Network for Moving Point Cloud Processing](https://arxiv.org/abs/1910.08287)
- [Using panoramic videos for multi-person localization and tracking in a 3D panoramic coordinate](https://arxiv.org/abs/1911.10535)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://arxiv.org/abs/1802.03426) [[Notes](paper_notes/umap.md)] (dimension reduction, better than t-SNE)
- [Deep Cuboid Detection: Beyond 2D Bounding Boxes](https://arxiv.org/abs/1611.10010) (Magic Leap)
- [Viewpoints and Keypoints](https://arxiv.org/abs/1411.6067) (Malik)
- [Lifting Object Detection Datasets into 3D](https://arxiv.org/abs/1503.06465) (PASCAL)
- [3D Object Class Detection in the Wild](https://arxiv.org/abs/1503.05038) (keypoint based)
- [Fast Single Shot Detection and Pose Estimation](https://arxiv.org/abs/1609.05590) 3DV 2016 (SSD + pose, Wei Liu)
- [Towards Scene Understanding with Detailed 3D Object Representations](https://arxiv.org/abs/1411.5935) IJCV 2014 (keypoint, 3D bbox annotation)
- [Virtual KITTI 2](https://arxiv.org/abs/2001.10773)
- [Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing](https://arxiv.org/abs/1612.02699) CVPR 2017
- [A Mixed Classification-Regression Framework for 3D Pose Estimation from 2D Images](https://arxiv.org/abs/1805.03225) BMVC 2018 (multi-bin, what's new?)

## 2020-02 (12)
- [Associative Embedding: End-to-End Learning for Joint Detection and Grouping](https://arxiv.org/abs/1611.05424) [[Notes](paper_notes/associative_embedding.md)] NIPS 2017
- [Pixels to Graphs by Associative Embedding](https://arxiv.org/abs/1706.07365) [[Notes](paper_notes/pixels_to_graphs.md)] NIPS 2017
- [Social LSTM: Human Trajectory Prediction in Crowded Spaces](http://cvgl.stanford.edu/papers/CVPR16_Social_LSTM.pdf) [[Notes](paper_notes/social_lstm.md)] CVPR 2017 
- [Online Video Object Detection using Association LSTM](http://openaccess.thecvf.com/content_ICCV_2017/papers/Lu__Online_Video_ICCV_2017_paper.pdf) [[Notes](paper_notes/association_lstm.md)] [single stage, recurrent]
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://arxiv.org/abs/1712.07629) [[Notes](paper_notes/superpoint.md)] CVPR 2018 (channel-to-pixel, deep SLAM, Magic Leap)
- [PointRend: Image Segmentation as Rendering](https://arxiv.org/abs/1912.08193) [[Notes](paper_notes/pointrend.md)] [FAIR]
- [GhostNet: More Features from Cheap Operations](https://arxiv.org/abs/1911.11907) [[Notes](paper_notes/ghostnet.md)] CVPR 2020
- [Multigrid: A Multigrid Method for Efficiently Training Video Models](https://arxiv.org/abs/1912.00998) [[Notes](paper_notes/multigrid_training.md)] CVPR 2020 (FAIR)
- [FixRes: Fixing the train-test resolution discrepancy](https://arxiv.org/abs/1906.06423) [[Notes](paper_notes/fixres.md)] NIPS 2019 (FAIR)
- [VirtualCam: Single-Stage Monocular 3D Object Detection with Virtual Cameras](https://arxiv.org/abs/1912.08035) [[Notes](paper_notes/virtual_cam.md)] [Mapillary, Mono3D]
- [Amodal Completion and Size Constancy in Natural Scenes](https://arxiv.org/abs/1509.08147) [[Notes](paper_notes/amodal_completion.md)] ICCV 2015 (Amodal completion)
- [MoCo: Momentum Contrast for Unsupervised Visual Representation Learning](https://arxiv.org/abs/1911.05722) [[Notes](paper_notes/moco.md)] [FAIR, Kaiming He]


## 2020-01 (19)
- [Double Descent: Reconciling modern machine learning practice and the bias-variance trade-of](https://arxiv.org/abs/1812.11118) [[Notes](paper_notes/double_descent.md)] PNAS 2019
- [Deep Double Descent: Where Bigger Models and More Data Hurt](https://arxiv.org/abs/1912.02292) [[Notes](paper_notes/deep_double_descent.md)]
- [Visualizing the Loss Landscape of Neural Nets](https://arxiv.org/abs/1712.09913) NIPS 2018
- [The ApolloScape Open Dataset for Autonomous Driving and its Application](https://arxiv.org/pdf/1803.06184.pdf) CVPR 2018 (dataset)
- [ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving](https://arxiv.org/abs/1811.12222) [[Notes](paper_notes/apollocar3d.md)] CVPR 2019
- [Part-level Car Parsing and Reconstruction from a Single Street View](https://arxiv.org/abs/1811.10837) [[Notes](paper_notes/apollo_car_parts.md)] [Baidu]
- [6D-VNet: End-to-end 6DoF Vehicle Pose Estimation from Monocular RGB Images](http://openaccess.thecvf.com/content_CVPRW_2019/papers/Autonomous%20Driving/Wu_6D-VNet_End-to-End_6-DoF_Vehicle_Pose_Estimation_From_Monocular_RGB_Images_CVPRW_2019_paper.pdf) [[Notes](paper_notes/6d_vnet.md)] CVPR 2019
- [RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving](https://arxiv.org/abs/2001.03343) [[Notes](paper_notes/rtm3d.md)]
- [DORN: Deep Ordinal Regression Network for Monocular Depth Estimation](https://arxiv.org/abs/1806.02446) [[Notes](paper_notes/dorn.md)] CVPR 2018 
- [D&T: Detect to Track and Track to Detect](https://arxiv.org/abs/1710.03958) [[Notes](paper_notes/detect_track.md)] ICCV 2017 (from Feichtenhofer)
- [CRF-Net: A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection](https://ieeexplore.ieee.org/abstract/document/8916629/) [[Notes](paper_notes/crf_net.md)] SDF 2019 (radar detection)
- [RVNet: Deep Sensor Fusion of Monocular Camera and Radar for Image-based Obstacle Detection in Challenging Environments](https://www.researchgate.net/profile/Vijay_John3/publication/335833918_RVNet_Deep_Sensor_Fusion_of_Monocular_Camera_and_Radar_for_Image-based_Obstacle_Detection_in_Challenging_Environments/links/5d7f164e92851c87c38b09f1/RVNet-Deep-Sensor-Fusion-of-Monocular-Camera-and-Radar-for-Image-based-Obstacle-Detection-in-Challenging-Environments.pdf) [[Notes](paper_notes/rvnet.md)] PSIVT 2019
- [RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles](https://arxiv.org/abs/1905.00526) [[Notes](paper_notes/rrpn_radar.md)] ICIP 2019
- [ROLO: Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking](https://arxiv.org/abs/1607.05781) [[Notes](paper_notes/rolo.md)] ISCAS 2016
- [Recurrent SSD: Recurrent Multi-frame Single Shot Detector for Video Object Detection](https://www.merl.com/publications/docs/TR2018-137.pdf) [[Notes](paper_notes/recurrent_ssd.md)] BMVC 2018 (Mitsubishi)
- [Recurrent RetinaNet: A Video Object Detection Model Based on Focal Loss](https://doi.org/10.1007/978-3-030-04212-7_44) [[Notes](paper_notes/recurrent_retinanet.md)] ICONIP 2018 (single stage, recurrent)
- [Actions as Moving Points](https://arxiv.org/abs/2001.04608) [[Notes](paper_notes/moc.md)] [not suitable for online]
- [The PREVENTION dataset: a novel benchmark for PREdiction of VEhicles iNTentIONs](10.1109/ITSC.2019.8917433) [[Notes](paper_notes/prevention_dataset.md)] ITSC 2019
- [Semi-Automatic High-Accuracy Labelling Tool for Multi-Modal Long-Range Sensor Dataset](https://sci-hub.tw/10.1109/IVS.2018.8500672) [[Notes](paper_notes/prevention_annotation.md)] IV 2018
- [Astyx dataset: Automotive Radar Dataset for Deep Learning Based 3D Object Detection](https://www.astyx.com/fileadmin/redakteur/dokumente/Automotive_Radar_Dataset_for_Deep_learning_Based_3D_Object_Detection.PDF) [[Notes](paper_notes/astyx_dataset.md)] EuRAD 2019 (Astyx)
- [Astyx camera radar: Deep Learning Based 3D Object Detection for Automotive Radar and Camera](https://www.astyx.net/fileadmin/redakteur/dokumente/Deep_Learning_Based_3D_Object_Detection_for_Automotive_Radar_and_Camera.PDF) [[Notes](paper_notes/astyx_radar_camera_fusion.md)] EuRAD 2019 (Astyx)


## 2019-12 (12)
- [How Do Neural Networks See Depth in Single Images?](http://openaccess.thecvf.com/content_ICCV_2019/papers/van_Dijk_How_Do_Neural_Networks_See_Depth_in_Single_Images_ICCV_2019_paper.pdf) [[Notes](paper_notes/what_monodepth_see.md)] ICCV 2019
- [Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera](https://arxiv.org/abs/1807.00275) ICRA 2019  (depth completion)
- [DC: Depth Coefficients for Depth Completion](https://arxiv.org/abs/1903.05421) [[Notes](paper_notes/depth_coeff.md)] CVPR 2019 (Xiaoming Liu)
- [Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser Observation](https://arxiv.org/pdf/1611.02174.pdf) [[Notes](paper_notes/depth_from_one_line.md)] ICRA 2017
- [PointPainting: Sequential Fusion for 3D Object Detection](https://arxiv.org/pdf/1911.10150.pdf) (nuscenece)
- [VO-Monodepth: Enhancing self-supervised monocular depth estimation with traditional visual odometry](https://arxiv.org/abs/1908.03127) [[Notes](paper_notes/vo_monodepth.md)] 3DV 2019 (sparse to dense)
- [Probabilistic Object Detection: Definition and Evaluation](https://arxiv.org/abs/1811.10800) [[Notes](paper_notes/pdq.md)]
- [The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation](https://arxiv.org/abs/1904.03215) [[Notes](paper_notes/fishyscape.md)] ICCV 2019
- [On Calibration of Modern Neural Networks](https://arxiv.org/abs/1706.04599) [[Notes](paper_notes/calib_modern_nn.md)] ICML 2017 (Weinberger)
- [Extreme clicking for efficient object annotation](https://arxiv.org/abs/1708.02750) [[Notes](paper_notes/extreme_clicking.md)] ICCV 2017
- [Radar and Camera Early Fusion for Vehicle Detection in Advanced Driver Assistance Systems](https://ml4ad.github.io/files/papers/Radar%20and%20Camera%20Early%20Fusion%20for%20Vehicle%20Detection%20in%20Advanced%20Driver%20Assistance%20Systems.pdf) [[Notes](paper_notes/radar_camera_qcom.md)] NeurIPS 2019 (radar)
- [Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector](https://arxiv.org/abs/1901.10609) [[Notes](paper_notes/deep_active_learning_lidar.md)] IV 2019
- [C3DPO: Canonical 3D Pose Networks for Non-Rigid Structure From Motion](https://arxiv.org/abs/1909.02533) [[Notes](paper_notes/c3dpo.md)] ICCV 2019
- [YOLACT: Real-time Instance Segmentation](https://arxiv.org/abs/1904.02689) [[Notes](paper_notes/yolact.md)] ICCV 2019
- [YOLACT++: Better Real-time Instance Segmentation](https://arxiv.org/abs/1912.06218)


## 2019-11 (20)
- [Review of Image and Feature Descriptors](paper_notes/review_descriptors.md)
- [Vehicle Detection With Automotive Radar Using Deep Learning on Range-Azimuth-Doppler Tensors](http://openaccess.thecvf.com/content_ICCVW_2019/papers/CVRSUAD/Major_Vehicle_Detection_With_Automotive_Radar_Using_Deep_Learning_on_Range-Azimuth-Doppler_ICCVW_2019_paper.pdf) [[Notes](paper_notes/radar_fft_qcom.md)] ICCV 2019
- [GPP: Ground Plane Polling for 6DoF Pose Estimation of Objects on the Road](https://arxiv.org/abs/1811.06666) [[Notes](paper_notes/gpp.md)] IV 2020 [UCSD, Trevidi, mono 3DOD]
- [MVRA: Multi-View Reprojection Architecture for Orientation Estimation](http://openaccess.thecvf.com/content_ICCVW_2019/papers/ADW/Choi_Multi-View_Reprojection_Architecture_for_Orientation_Estimation_ICCVW_2019_paper.pdf) [[Notes](paper_notes/mvra.md)] ICCV 2019
- [YOLOv3: An Incremental Improvement](https://pjreddie.com/media/files/papers/YOLOv3.pdf)
- [Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving](https://arxiv.org/abs/1904.04620) [[Notes](paper_notes/gaussian_yolov3.md)] ICCV 2019 (Detection with Uncertainty)
- [Bayesian YOLOv3: Uncertainty Estimation in One-Stage Object Detection](https://arxiv.org/abs/1905.10296) [[Notes](paper_notes/bayesian_yolov3.md)] [DriveU]
- [Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection](https://arxiv.org/abs/1804.05132) [[Notes](paper_notes/towards_safe_ad.md)] ITSC 2018 (DriveU)
- [Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection](https://arxiv.org/abs/1809.05590) [[Notes](paper_notes/towards_safe_ad2.md)] IV 2019 (DriveU)
- [Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving](https://arxiv.org/abs/1909.12358) [[Notes](paper_notes/towards_safe_ad_calib.md)] IROS 2019 (DriveU)
- [LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving](https://arxiv.org/abs/1903.08701) [[Notes](paper_notes/lasernet.md)] CVPR 2019 (uncertainty)
- [LaserNet KL: Learning an Uncertainty-Aware Object Detector for Autonomous Driving](https://arxiv.org/abs/1910.11375) [[Notes](paper_notes/lasernet_kl.md)] [LaserNet with KL divergence]
- [IoUNet: Acquisition of 	Localization Confidence for Accurate Object Detection](https://arxiv.org/abs/1807.11590) [[Notes](paper_notes/iou_net.md)] ECCV 2018
- [gIoU: Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://arxiv.org/abs/1902.09630) [[Notes](paper_notes/giou.md)] CVPR 2019
- [KL Loss: Bounding Box Regression with Uncertainty for Accurate Object Detection](https://arxiv.org/abs/1809.08545) [[Notes](paper_notes/kl_loss.md)] CVPR 2019
- [CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth](https://arxiv.org/abs/1904.02028) [[Notes](paper_notes/cam_conv.md)] CVPR 2019
- [BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors](https://arxiv.org/abs/1903.03838) [[Notes](paper_notes/bayes_od.md)]
- [TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching](https://arxiv.org/abs/1906.04463) [[Notes](paper_notes/twsm_net.md)] ICIP 2019
- [Accurate Uncertainties for Deep Learning Using Calibrated Regression](https://arxiv.org/abs/1807.00263) [[Notes](paper_notes/dl_regression_calib.md)] ICML 2018
- [Calibrating Uncertainties in Object Localization Task](https://arxiv.org/abs/1811.11210) [[Notes](paper_notes/2dod_calib.md)] NIPS 2018
- [SMWA: On the Over-Smoothing Problem of CNN Based Disparity Estimation](http://openaccess.thecvf.com/content_ICCV_2019/papers/Chen_On_the_Over-Smoothing_Problem_of_CNN_Based_Disparity_Estimation_ICCV_2019_paper.pdf) [[Notes](paper_notes/smwa.md)] ICCV 2019 (depth estimation)
- [Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image](https://arxiv.org/abs/1709.07492) [[Notes](paper_notes/sparse_to_dense.md)] ICRA 2018 (depth completion)


## 2019-10 (18)
- [Review of monocular object detection](paper_notes/review_mono_3dod.md)
- [Review of 2D 3D contraints in Mono 3DOD](paper_notes/mono_3dod_2d3d_constraints.md)
- [MonoGRNet 2: Monocular 3D Object Detection via Geometric Reasoning on Keypoints](https://arxiv.org/abs/1905.05618) [[Notes](paper_notes/monogrnet_russian.md)] [estimates depth from keypoints]
- [Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image](https://arxiv.org/abs/1703.07570) [[Notes](paper_notes/deep_manta.md)] CVPR 2017
- [SS3D: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss](https://arxiv.org/abs/1906.08070) [[Notes](paper_notes/ss3d.md)] [rergess distance from images, centernet like]
- [GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving](https://arxiv.org/abs/1903.10955) [[Notes](paper_notes/gs3d.md)] CVPR 2019
- [M3D-RPN: Monocular 3D Region Proposal Network for Object Detection](https://arxiv.org/abs/1907.06038) [[Notes](paper_notes/m3d_rpn.md)] ICCV 2019 (Xiaoming Liu)
- [TLNet: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection](https://arxiv.org/abs/1906.01193) [[Notes](paper_notes/tlnet.md)] CVPR 2019
- [A Survey on 3D Object Detection Methods for Autonomous Driving Applications](http://wrap.warwick.ac.uk/114314/1/WRAP-survey-3D-object-detection-methods-autonomous-driving-applications-Arnold-2019.pdf) [[Notes](paper_notes/3dod_review.md)] TITS 2019 [Review]
- [BEV-IPM: Deep Learning based Vehicle Position and Orientation Estimation via Inverse Perspective Mapping Image](https://ieeexplore.ieee.org/abstract/document/8814050) [[Notes](paper_notes/bev_od_ipm.md)] IV 2019
- [ForeSeE: Task-Aware Monocular Depth Estimation for 3D Object Detection](https://arxiv.org/abs/1909.07701) [[Notes](paper_notes/foresee_mono3dod.md)] AAAI 2020 oral [successor to pseudo-lidar, mono 3DOD SOTA]
- [Obj-dist: Learning Object-specific Distance from a Monocular Image](https://arxiv.org/abs/1909.04182) [[Notes](paper_notes/obj_dist_iccv2019.md)] ICCV 2019 (xmotors.ai + NYU)
- [DisNet: A novel method for distance estimation from monocular camera](https://project.inria.fr/ppniv18/files/2018/10/paper22.pdf) [[Notes](paper_notes/disnet.md)] IROS 2018
- [BirdGAN: Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles](https://arxiv.org/abs/1904.08494) [[Notes](paper_notes/birdgan.md)] IROS 2019 
- [Shift R-CNN: Deep Monocular 3D Object Detection with Closed-Form Geometric Constraints](https://arxiv.org/abs/1905.09970) [[Notes](paper_notes/shift_rcnn.md)] ICIP 2019
- [3D-RCNN: Instance-level 3D Object Reconstruction via Render-and-Compare](http://openaccess.thecvf.com/content_cvpr_2018/papers/Kundu_3D-RCNN_Instance-Level_3D_CVPR_2018_paper.pdf) [[Notes](paper_notes/3d_rcnn.md)] CVPR 2018
- [Deep Optics for Monocular Depth Estimation and 3D Object Detection](https://arxiv.org/abs/1904.08601) [[Notes](paper_notes/deep_optics.md)] ICCV 2019
- [MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation](https://arxiv.org/abs/1906.06059) [[Notes](paper_notes/monoloco.md)] ICCV 2019
- [Joint Monocular 3D Vehicle Detection and Tracking](https://arxiv.org/abs/1811.10742) [[Notes](paper_notes/mono_3d_tracking.md)] ICCV 2019 (Berkeley DeepDrive)
- [CasGeo: 3D Bounding Box Estimation for Autonomous Vehicles by Cascaded Geometric Constraints and Depurated 2D Detections Using 3D Results](https://arxiv.org/abs/1909.01867) [[Notes](paper_notes/casgeo.md)]




## 2019-09 (17)
- [Slimmable Neural Networks](https://arxiv.org/abs/1812.08928) [[Notes](paper_notes/slimmable_networks.md)] ICLR 2019
- [Universally Slimmable Networks and Improved Training Techniques](https://arxiv.org/abs/1903.05134) [[Notes](paper_notes/universal_slimmable.md)] ICCV 2019
- [AutoSlim: Towards One-Shot Architecture Search for Channel Numbers](https://arxiv.org/abs/1903.11728)
- [Once for All: Train One Network and Specialize it for Efficient Deployment](https://arxiv.org/pdf/1908.09791.pdf)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://vision.cornell.edu/se3/wp-content/uploads/2018/03/2666.pdf) [[Notes](paper_notes/dota.md)] CVPR 2018 (rotated bbox)
- [RoiTransformer: Learning RoI Transformer for Oriented Object Detection in Aerial Images](http://openaccess.thecvf.com/content_CVPR_2019/papers/Ding_Learning_RoI_Transformer_for_Oriented_Object_Detection_in_Aerial_Images_CVPR_2019_paper.pdf) [[Notes](paper_notes/roi_transformer.md)] CVPR 2019 (rotated bbox)
- [RRPN: Arbitrary-Oriented Scene Text Detection via Rotation Proposals](https://arxiv.org/abs/1703.01086) TMM 2018
- [R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection](https://arxiv.org/abs/1706.09579) (rotated bbox)
- [TI white paper: Webinar: mmWave Radar for Automotive and Industrial applications
](https://training.ti.com/epd-pro-rap-mmwaveradar-adh-tr-webinar-eu) [[Notes](paper_notes/ti_mmwave_radar_webinar.md)] [TI, radar]
- [Federated Learning: Strategies for Improving Communication Efficiency](https://arxiv.org/abs/1610.05492) [[Notes](paper_notes/federated_learning_comm.md)] NIPS 2016
- [sort: Simple Online and Realtime Tracking](https://arxiv.org/abs/1602.00763) [[Notes](paper_notes/sort.md)] ICIP 2016
- [deep-sort: Simple Online and Realtime Tracking with a Deep Association Metric](https://arxiv.org/abs/1703.07402) [[Notes](paper_notes/deep_sort.md)]
- [MT-CNN: Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://kpzhang93.github.io/MTCNN_face_detection_alignment/) [[Notes](paper_notes/mtcnn.md)] SPL 2016 (real time, facial landmark)
- [RetinaFace: Single-stage Dense Face Localisation in the Wild](https://arxiv.org/abs/1905.00641) [[Notes](paper_notes/retina_face.md)] [joint object and landmark detection]
- [SC-SfM-Learner: Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video](https://arxiv.org/abs/1908.10553) [[Notes](paper_notes/sc_sfm_learner.md)] NIPS 2019
- [SiamMask: Fast Online Object Tracking and Segmentation: A Unifying Approach](https://arxiv.org/abs/1812.05050) CVPR 2019 (tracking, segmentation, label propagation)
- [Review of Klmn Filter](https://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/) (from Tim Babb, Pixar Animation) [[Notes](paper_notes/kalman_filter.md)]
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://arxiv.org/abs/1605.06409) [[Notes](paper_notes/rfcn.md)] NIPS 2016
- [Guided backprop: Striving for Simplicity: The All Convolutional Net](https://arxiv.org/pdf/1412.6806.pdf) [[Notes](paper_notes/guided_backprop.md)] ICLR 2015
- [Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks](http://www.cs.cmu.edu/~mvo/index_files/Papers/ONet_19.pdf) [[Notes](paper_notes/occlusion_net.md)] CVPR 2019
- [Boxy Vehicle Detection in Large Images](https://boxy-dataset.com/boxy/index) [[Notes](paper_notes/boxy.md)] ICCV 2019
- [FQNet: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection](https://arxiv.org/abs/1904.12681) [[Notes](paper_notes/fqnet.md)] CVPR 2019 (Mono 3DOD, Jiwen Lu)


## 2019-08 (18)
- [Mono3D: Monocular 3D Object Detection for Autonomous Driving](https://www.cs.toronto.edu/~urtasun/publications/chen_etal_cvpr16.pdf) [[Notes](paper_notes/mono3d.md)] CVPR2016
- [MonoDIS: Disentangling Monocular 3D Object Detection](https://arxiv.org/abs/1905.12365) [[Notes](paper_notes/monodis.md)] ICCV 2019
- [Pseudo lidar-e2e: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud](https://arxiv.org/abs/1903.09847) [[Notes](paper_notes/pseudo_lidar_e2e.md)] ICCV 2019 (pseudo-lidar with 2d and 3d consistency loss, better than PL and worse than PL++, SOTA for pure mono3D)
- [MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization](https://arxiv.org/abs/1811.10247) [[Notes](paper_notes/monogrnet.md)] AAAI 2019 (SOTA of Mono3DOD, MLF < MonoGRNet < Pseudo-lidar)
- [MLF: Multi-Level Fusion based 3D Object Detection from Monocular Images](http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_Multi-Level_Fusion_Based_CVPR_2018_paper.pdf) [[Notes](paper_notes/mlf.md)] CVPR 2018 (precursor to pseudo-lidar)
- [ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape](https://arxiv.org/abs/1812.02781) [[Notes](paper_notes/roi10d.md)] CVPR 2019
- [Accurate Monocular 3D Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving](https://arxiv.org/abs/1903.11444) [[Notes](paper_notes/pseudo_lidar_color.md)] ICCV 2019 [similar to pseudo-lidar, color-enhanced]
- [Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors](https://arxiv.org/abs/1901.03446) [[Notes](paper_notes/mono3d++.md)] (from Stefano Soatto) AAAI 2019
- [Deep Metadata Fusion for Traffic Light to Lane Assignment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8613841) [[Notes](paper_notes/deep_lane_association.md)] IEEE RA-L 2019 (traffic lights association)
- [Automatic Traffic Light to Ego Vehicle Lane Association at Complex Intersections](https://ieeexplore.ieee.org/document/8569421) ITSC 2019 (traffic lights association)
- [Distant Vehicle Detection Using Radar and Vision](https://arxiv.org/abs/1901.10951)[[Notes](paper_notes/distant_object_radar.md)] ICRA 2019 [radar, vision, radar tracklets fusion]
- [Distance Estimation of Monocular Based on Vehicle Pose Information](https://iopscience.iop.org/article/10.1088/1742-6596/1168/3/032040/pdf) [[Notes](paper_notes/distance_estimation_pose_radar.md)]
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://arxiv.org/abs/1705.07115) [[Notes](paper_notes/uncertainty_multitask.md)] CVPR 2018 (Alex Kendall)
- [GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks](https://arxiv.org/abs/1711.02257) [[Notes](paper_notes/gradnorm.md)] ICML 2018 (multitask)
- [DTP: Dynamic Task Prioritization for Multitask Learning](http://openaccess.thecvf.com/content_ECCV_2018/papers/Michelle_Guo_Focus_on_the_ECCV_2018_paper.pdf) [[Notes](paper_notes/dtp.md)] ECCV 2018 [multitask, Stanford]
- [Will this car change the lane? - Turn signal recognition in the frequency domain](https://ieeexplore.ieee.org/document/6856477/) [[Notes](paper_notes/tsl_frequency.md)] IV 2014
- [Complex-YOLO: Real-time 3D Object Detection on Point Clouds](https://arxiv.org/abs/1803.06199) [[Notes](paper_notes/complex_yolo.md)] (BEV detection only)
- [Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds](https://arxiv.org/abs/1904.07537) CVPR 2019 (sensor fusion and tracking)
- [An intriguing failing of convolutional neural networks and the CoordConv solution](https://arxiv.org/abs/1807.03247) [[Notes](paper_notes/coord_conv.md)] NIPS 2018


## 2019-07 (19)
- [Deep Parametric Continuous Convolutional Neural Networks](http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Deep_Parametric_Continuous_CVPR_2018_paper.pdf) [[Notes](paper_notes/parametric_cont_conv.md)] CVPR 2018 (@Uber, sensor fusion)
- [ContFuse: Deep Continuous Fusion for Multi-Sensor 3D Object Detection](http://openaccess.thecvf.com/content_ECCV_2018/papers/Ming_Liang_Deep_Continuous_Fusion_ECCV_2018_paper.pdf) [[Notes](paper_notes/contfuse.md)] ECCV 2018 (@Uber, sensor fusion, birds eye view)
- [Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net](http://openaccess.thecvf.com/content_cvpr_2018/papers/Luo_Fast_and_Furious_CVPR_2018_paper.pdf) [[Notes](paper_notes/faf.md)] CVPR 2018 oral (lidar only, perception and prediction)
- [Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras](https://arxiv.org/pdf/1904.04998.pdf) [[Notes](paper_notes/mono_depth_video_in_the_wild.md)] ICCV 2019 [monocular depth estimation, intrinsic estimation, SOTA]
- [monodepth: Unsupervised Monocular Depth Estimation with Left-Right Consistency](https://arxiv.org/abs/1609.03677) [[Notes](paper_notes/monodepth.md)] CVPR 2017 oral (monocular depth estimation, stereo for training)
- [Struct2depth: Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos](https://arxiv.org/pdf/1811.06152.pdf) [[Notes](paper_notes/struct2depth.md)] AAAI 2019 (monocular depth estimation, estimating movement of dynamic object)
- [Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistency](https://arxiv.org/pdf/1711.03665.pdf) [[Notes](paper_notes/edge_aware_depth_normal.md)] AAAI 2018 (monocular depth estimation, static assumption, surface normal)
- [LEGO Learning Edge with Geometry all at Once by Watching Videos](https://arxiv.org/pdf/1803.05648.pdf) [[Notes](paper_notes/lego.md)] CVPR 2018 spotlight (monocular depth estimation, static assumption, surface normal)
- [Object Detection and 3D Estimation via an FMCW Radar Using a Fully Convolutional Network](https://arxiv.org/abs/1902.05394) [[Notes](paper_notes/radar_3d_od_fcn.md)] (radar, RD map, OD, Arxiv 201902) 
- [A study on Radar Target Detection Based on Deep Neural Networks](https://www.researchgate.net/publication/330748053_A_Study_on_Radar_Target_Detection_Based_on_Deep_Neural_Networks) [[Notes](paper_notes/radar_target_detection_tsinghua.md)] (radar, RD map, OD) 
- [2D Car Detection in Radar Data with PointNets](https://arxiv.org/abs/1904.08414) [[Notes](paper_notes/radar_detection_pointnet.md)] (from Ulm Univ, radar, point cloud, OD, Arxiv 201904) 
- [Learning Confidence for Out-of-Distribution Detection in Neural Networks](https://arxiv.org/abs/1802.04865) [[Notes](paper_notes/learning_ood_conf.md)] (budget to cheat)
- [A Deep Learning Approach to Traffic Lights: Detection, Tracking, and Classification](assets/papers/bosch_traffic_lights.pdf) [[Notes](paper_notes/bosch_traffic_lights.md)] ICRA 2017 (Bosch, traffic lights)
- [How hard can it be? Estimating the difficulty of visual search in an image](https://arxiv.org/abs/1705.08280) [[Notes](paper_notes/how_hard_can_it_be.md)] CVPR 2016
- [Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges](https://arxiv.org/pdf/1902.07830.pdf) [[Notes](paper_notes/deep_fusion_review.md)] (review from Bosch)
- [Review of monocular 3d object detection](https://zhuanlan.zhihu.com/p/57029694) (blog from )
- [Deep3dBox: 3D Bounding Box Estimation Using Deep Learning and Geometry](https://arxiv.org/abs/1612.00496) [[Notes](paper_notes/deep3dbox.md)] CVPR 2017 [Zoox]
- [MonoPSR: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction](https://arxiv.org/abs/1904.01690) [[Notes](paper_notes/monopsr.md)] CVPR 2019
- [OFT: Orthographic Feature Transform for Monocular 3D Object Detection](https://arxiv.org/abs/1811.08188) [[Notes](paper_notes/oft.md)] BMVC 2019 [Convert camera to BEV, Alex Kendall]


## 2019-06 (12)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://arxiv.org/abs/1905.02249) [[Notes](paper_notes/MixMatch.md)]
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/pdf/1905.11946.pdf) [[Notes](paper_notes/efficientnet.md)] ICML 2019
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://arxiv.org/pdf/1703.04977.pdf) [[Notes](paper_notes/uncertainty_bdl.md)] NIPS 2017
- [Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding](https://arxiv.org/pdf/1511.02680.pdf) [[Notes](paper_notes/bayesian_segnet.md)]BMVC 2017
- [TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents](https://arxiv.org/pdf/1811.02146.pdf) [[Notes](paper_notes/trafficpredict.md)] AAAI 2019 oral
- [Deep Depth Completion of a Single RGB-D Image](https://arxiv.org/pdf/1803.09326.pdf) [[Notes](paper_notes/deep_depth_completion_rgbd.md)] CVPR 2018 (indoor)
- [DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image](https://arxiv.org/pdf/1812.00488v2.pdf) [[Notes](paper_notes/deeplidar.md)] CVPR 2019 (outdoor)
- [SfMLearner: Unsupervised Learning of Depth and Ego-Motion from Video](https://people.eecs.berkeley.edu/~tinghuiz/projects/SfMLearner/cvpr17_sfm_final.pdf) [[Notes](paper_notes/sfm_learner.md)] CVPR 2017
- [Monodepth2: Digging Into Self-Supervised Monocular Depth Estimation](https://arxiv.org/abs/1806.01260) [[Notes](paper_notes/monodepth2.md)] ICCV 2019 [Niantic]
- [DeepSignals: Predicting Intent of Drivers Through Visual Signals](https://arxiv.org/pdf/1905.01333.pdf) [[Notes](paper_notes/deep_signals.md)] ICRA 2019 (@Uber, turn signal detection)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://arxiv.org/pdf/1904.01355.pdf) [[Notes](paper_notes/fcos.md)] ICCV 2019
- [Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving](https://arxiv.org/abs/1906.06310) [[Notes](paper_notes/pseudo_lidar++.md)] ICLR 2020
- [MMF: Multi-Task Multi-Sensor Fusion for 3D Object Detection](http://www.cs.toronto.edu/~byang/papers/mmf.pdf) [[Notes](paper_notes/mmf.md)] CVPR 2019 (@Uber, sensor fusion)


## 2019-05 (18)
- [CenterNet: Objects as points](https://arxiv.org/pdf/1904.07850.pdf) (from ExtremeNet authors) [[Notes](paper_notes/centernet_ut.md)]
- [CenterNet: Object Detection with Keypoint Triplets](https://arxiv.org/pdf/1904.08189.pdf) [[Notes](paper_notes/centernet_cas.md)]
- [Object Detection based on Region Decomposition and Assembly](https://arxiv.org/pdf/1901.08225.pdf) [[Notes](paper_notes/object_detection_region_decomposition.md)] AAAI 2019 
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://arxiv.org/abs/1803.03635) [[Notes](paper_notes/lottery_ticket_hypothesis.md)] ICLR 2019 
- [M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network](https://arxiv.org/abs/1811.04533) [[Notes](paper_notes/m2det.md)] AAAI 2019 
- [Deep Radar Detector](https://arxiv.org/abs/1906.12187) [[Notes](paper_notes/deep_radar_detector.md)] RadarCon 2019
- [Semantic Segmentation on Radar Point Clouds](https://ieeexplore.ieee.org/document/8455344) [[[Notes](paper_notes/radar_point_semantic_seg.md)]] (from Daimler AG) FUSION 2018
- [Pruning Filters for Efficient ConvNets](https://arxiv.org/pdf/1608.08710.pdf) [[Notes](paper_notes/pruning_filters.md)] ICLR 2017
- [Layer-compensated Pruning for Resource-constrained Convolutional Neural Networks](https://arxiv.org/pdf/1810.00518.pdf) [[Notes](paper_notes/layer_compensated_pruning.md)] NIPS 2018 talk
- [LeGR: Filter Pruning via Learned Global Ranking](https://arxiv.org/pdf/1904.12368.pdf) [[Notes](paper_notes/legr.md)]
- [NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection](https://arxiv.org/pdf/1904.07392.pdf) [[Notes](paper_notes/nas_fpn.md)] CVPR 2019 
- [AutoAugment: Learning Augmentation Policies from Data](https://arxiv.org/abs/1805.09501) [[Notes](paper_notes/autoaugment.md)] CVPR 2019 
- [Path Aggregation Network for Instance Segmentation](https://arxiv.org/pdf/1803.01534.pdf) [[Notes](paper_notes/panet.md)] CVPR 2018 
- [Channel Pruning for Accelerating Very Deep Neural Networks](https://arxiv.org/pdf/1707.06168.pdf) ICCV 2017 (Face++, Yihui He) [[Notes](paper_notes/channel_pruning_megvii.md)]
- [AMC: AutoML for Model Compression and Acceleration on Mobile Devices](https://arxiv.org/pdf/1802.03494.pdf) ECCV 2018 (Song Han, Yihui He)
- [MobileNetV3: Searching for MobileNetV3](https://arxiv.org/pdf/1905.02244.pdf) [[Notes](paper_notes/mobilenets_v3.md)]
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://arxiv.org/pdf/1807.11626.pdf) [[Notes](mnasnet.md)] CVPR 2019 
- [Rethinking the Value of Network Pruning](https://arxiv.org/pdf/1810.05270.pdf) ICLR 2019

## 2019-04 (12)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/pdf/1801.04381.pdf) (MobileNets v2) [[Notes](paper_notes/mobilenets_v2.md)] CVPR 2018
- [A New Performance Measure and Evaluation Benchmark
for Road Detection Algorithms](http://www.cvlibs.net/publications/Fritsch2013ITSC.pdf) [[Notes](paper_notes/kitti_lane.md)] ITSC 2013
- [MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving](https://arxiv.org/pdf/1612.07695.pdf) [[Notes](paper_notes/multinet_raquel.md)]
- [Optimizing the Trade-off between Single-Stage and Two-Stage Object Detectors using Image Difficulty Prediction](https://arxiv.org/pdf/1803.08707.pdf) (Very nice illustration of 1 and 2 stage object detection)
- [Light-Head R-CNN: In Defense of Two-Stage Object Detector](https://arxiv.org/pdf/1711.07264.pdf) [[Notes](paper_notes/lighthead_rcnn.md)] (from Megvii)
- [CSP: High-level Semantic Feature Detection: A New Perspective for Pedestrian Detection](https://arxiv.org/pdf/1904.02948.pdf) (center and scale prediction) [[Notes](paper_notes/csp_pedestrian.md)] CVPR 2019 
- [Review of Anchor-free methods (Blog) Anchor-Free](https://zhuanlan.zhihu.com/p/62103812) [Anchor free](https://zhuanlan.zhihu.com/p/64563186) [My Slides on CSP](https://docs.google.com/presentation/d/1_dUfxv63108bZXUnVYPIOAdEIkRZw5BR9-rOp-Ni0X0/)
- [DenseBox: Unifying Landmark Localization with End to End Object Detection](https://arxiv.org/pdf/1509.04874.pdf)
- [CornerNet: Detecting Objects as Paired Keypoints](https://arxiv.org/pdf/1808.01244.pdf) [[Notes](paper_notes/cornernet.md)] ECCV 2018
- [ExtremeNet: Bottom-up Object Detection by Grouping Extreme and Center Points](https://arxiv.org/pdf/1901.08043.pdf) [[Notes](paper_notes/extremenet.md)] CVPR 2019
- [FSAF: Feature Selective Anchor-Free Module for Single-Shot Object Detection](https://arxiv.org/pdf/1903.00621.pdf) [[Notes](paper_notes/fsaf_detection.md)] CVPR 2019
- [FoveaBox: Beyond Anchor-based Object Detector](https://arxiv.org/pdf/1904.03797v1.pdf) (anchor-free) [[Notes](paper_notes/foveabox.md)]



## 2019-03 (19)
- [Bag of Freebies for Training Object Detection Neural Networks](https://arxiv.org/pdf/1902.04103.pdf) [[Notes](paper_notes/bag_of_freebies_object_detection.md)]
- [mixup: Beyond Empirical Risk Minimization](https://arxiv.org/pdf/1710.09412.pdf) [[Notes](paper_notes/mixup.md)] ICLR 2018
- [Multi-view Convolutional Neural Networks for 3D Shape Recognition](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Su_Multi-View_Convolutional_Neural_ICCV_2015_paper.pdf) (MVCNN) [[Notes](paper_notes/mvcnn.md)] ICCV 2015 
- [3D ShapeNets: A Deep Representation for Volumetric Shapes](http://3dshapenets.cs.princeton.edu/paper.pdf) [[Notes](paper_notes/3d_shapenets.md)] CVPR 2015
- [Volumetric and Multi-View CNNs for Object Classification on 3D Data](https://arxiv.org/pdf/1604.03265.pdf) [[Notes](paper_notes/vol_vs_mvcnn.md)] CVPR 2016
- [Group Normalization](https://arxiv.org/pdf/1803.08494.pdf) [[Notes](paper_notes/groupnorm.md)] ECCV 2018
- [Spatial Transformer Networks](https://arxiv.org/pdf/1506.02025.pdf) [[Notes](paper_notes/stn.md)] NIPS 2015
- [Frustum PointNets for 3D Object Detection from RGB-D Data](https://arxiv.org/pdf/1711.08488.pdf) (F-PointNet) [[Notes](paper_notes/frustum_pointnet.md)] CVPR 2018 
- [Dynamic Graph CNN for Learning on Point Clouds](https://arxiv.org/pdf/1801.07829.pdf) [[Notes](paper_notes/edgeconv.md)]
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://arxiv.org/pdf/1812.04244v1.pdf) (SOTA for 3D object detection) [[Notes](paper_notes/point_rcnn.md)] CVPR 2019
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://arxiv.org/pdf/1611.07759.pdf) (MV3D) [[Notes](paper_notes/mv3d.md)] CVPR 2017 (Baidu, sensor fusion, BV proposal)
- [Joint 3D Proposal Generation and Object Detection from View Aggregation](https://arxiv.org/pdf/1712.02294.pdf) (AVOD) [[Notes](paper_notes/avod.md)] IROS 2018 (sensor fusion, multiview proposal)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/pdf/1704.04861.pdf) [[Notes](paper_notes/mobilenets.md)]
- [Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving](https://arxiv.org/abs/1812.07179) [[Notes](paper_notes/pseudo_lidar.md)] CVPR 2019
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://arxiv.org/pdf/1711.06396.pdf) CVPR 2018 (Apple, first end-to-end point cloud encoding to grid)
- [SECOND: Sparsely Embedded Convolutional Detection](https://www.mdpi.com/1424-8220/18/10/3337/pdf) Sensors 2018 (builds on VoxelNet)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://arxiv.org/pdf/1812.05784.pdf) [[Notes](paper_notes/point_pillars.md)] CVPR 2019 (builds on SECOND)
- [Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite](http://www.cvlibs.net/publications/Geiger2012CVPR.pdf) [[Notes](paper_notes/kitti.md)] CVPR 2012
- [Vision meets Robotics: The KITTI Dataset](http://ww.cvlibs.net/publications/Geiger2013IJRR.pdf) [[Notes](paper_notes/kitti.md)] IJRR 2013


## 2019-02 (9)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://arxiv.org/pdf/1705.07750.pdf) (I3D) [[Notes](paper_notes/quo_vadis_i3d.md)]Video CVPR 2017
- [Initialization Strategies of Spatio-Temporal Convolutional Neural Networks](https://arxiv.org/pdf/1503.07274.pdf) [[Notes](paper_notes/quo_vadis_i3d.md)] Video
- [Detect-and-Track: Efficient Pose Estimation in Videos](https://arxiv.org/pdf/1712.09184.pdf) [[Notes](paper_notes/quo_vadis_i3d.md)] ICCV 2017 Video
- [Deep Learning Based Rib Centerline Extraction and Labeling](https://arxiv.org/pdf/1809.07082) [[Notes](paper_notes/rib_centerline_philips.md)] MI MICCAI 2018
- [SlowFast Networks for Video Recognition](https://arxiv.org/pdf/1812.03982.pdf) [[Notes](paper_notes/slowfast.md)] Video
- [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/pdf/1611.05431.pdf) (ResNeXt) [[Notes](paper_notes/resnext.md)] CVPR 2017
- [Beyond the pixel plane: sensing and learning in 3D](https://thegradient.pub/beyond-the-pixel-plane-sensing-and-learning-in-3d/) (blog, [](https://zhuanlan.zhihu.com/p/44386618))
- [VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition](https://www.ri.cmu.edu/pub_files/2015/9/voxnet_maturana_scherer_iros15.pdf) (VoxNet) [[Notes](paper_notes/voxnet.md)]
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://arxiv.org/pdf/1612.00593.pdf) CVPR 2017 [[Notes](paper_notes/pointnet.md)]
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://arxiv.org/pdf/1706.02413.pdf) NIPS 2017 [[Notes](paper_notes/pointnet++.md)]
- [Review of Geometric deep learning  (from )](https://zhuanlan.zhihu.com/p/36888114) (Up to CVPR 2018)


## 2019-01 (10)
- [Human-level control through deep reinforcement learning (Nature DQN paper)](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf) [[Notes](paper_notes/nature_dqn_paper.md)] DRL
- [Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection](https://arxiv.org/pdf/1811.08661.pdf) [[Notes](paper_notes/retina_unet.md)] MI
- [Panoptic Segmentation](https://arxiv.org/pdf/1801.00868.pdf) [[Notes](paper_notes/panoptic_segmentation.md)] PanSeg
- [Panoptic Feature Pyramid Networks](https://arxiv.org/pdf/1901.02446.pdf) [[Notes](paper_notes/panoptic_fpn.md)] PanSeg 
- [Attention-guided Unified Network for Panoptic Segmentation](https://arxiv.org/pdf/1812.03904.pdf) [[Notes](paper_notes/AUNet_panoptic.md)] PanSeg
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://arxiv.org/pdf/1812.01187.pdf) [[Notes](paper_notes/bag_of_tricks_cnn.md)] CLS
- [Deep Reinforcement Learning for Vessel Centerline Tracing in Multi-modality 3D Volumes](https://link.springer.com/chapter/10.1007/978-3-030-00937-3_86) [[Notes](paper_notes/drl_vessel_centerline.md)] DRL MI
- [Deep Reinforcement Learning for Flappy Bird](http://cs229.stanford.edu/proj2015/362_report.pdf) [[Notes](paper_notes/drl_flappy.md)] DRL
- [Long-Term Feature Banks for Detailed Video Understanding](https://arxiv.org/pdf/1812.05038.pdf) [[Notes](paper_notes/long_term_feat_bank.md)] Video 
- [Non-local Neural Networks](https://arxiv.org/pdf/1711.07971.pdf) [[Notes](paper_notes/non_local_net.md)] Video CVPR 2018


## 2018
- [Mask R-CNN](https://arxiv.org/pdf/1703.06870.pdf)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://arxiv.org/pdf/1712.00726.pdf)
- [Focal Loss for Dense Object Detection](https://arxiv.org/pdf/1708.02002.pdf) (RetinaNet) [[Notes](paper_notes/focal_loss.md)]
- [Squeeze-and-Excitation Networks](https://arxiv.org/pdf/1709.01507) (SENet)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://arxiv.org/pdf/1710.10196.pdf)
- [Deformable Convolutional Networks](https://arxiv.org/pdf/1703.06211.pdf) (build on R-FCN)
- [Learning Region Features for Object Detection](https://arxiv.org/pdf/1803.07066.pdf)

## 2017 and before
- [Learning notes on Deep Learning](Learning_notes.md)
- [List of Papers on Machine Learning](List_of_Machine_Learning_Papers.md)
- [Notes of Literature Review on CNN in CV](paper_notes/cnn_papers.md) This is the notes for all the papers in the recommended list [here](papers_and_books_to_start.md)
- [Notes of Literature Review (Others)](misc.md)
- [Notes on how to set up DL/ML environment](ML_DL_environment_Setup.md)
- [Useful setup notes](installation_log.md)

## Papers to Read
Here is the list of papers waiting to be read. 
### Deep Learning in general
- [SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving](https://arxiv.org/pdf/1612.01051.pdf)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://arxiv.org/pdf/1706.02677.pdf)
- [ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness](https://openreview.net/forum?id=Bygh9j09KX) ICML 2019
- [Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet](https://openreview.net/forum?id=SkfMWhAqYQ) (BagNet) [blog](https://blog.evjang.com/2019/02/bagnet.html) ICML 2019
- [A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay](https://arxiv.org/pdf/1803.09820v2.pdf)
- [Understanding deep learning requires rethinking generalization](https://arxiv.org/pdf/1611.03530.pdf)


### 2D Object detection
- [Mask Scoring R-CNN](https://arxiv.org/pdf/1903.00241.pdf) CVPR 2019
- [Training Region-based Object Detectors with Online Hard Example Mining](https://arxiv.org/pdf/1604.03540.pdf)

### Instance and Panoptic Segmentation
- [TensorMask: A Foundation for Dense Object Segmentation](https://arxiv.org/pdf/1903.12174.pdf)


### Video Understanding
- [Learning Spatiotemporal Features with 3D Convolutional Networks](https://arxiv.org/pdf/1412.0767.pdf) (C3D)  VideoICCV 2015 
- [AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions](https://arxiv.org/pdf/1705.08421.pdf)
- [Spatiotemporal Residual Networks for Video Action Recognition](https://arxiv.org/pdf/1611.02155.pdf) (decouple spatiotemporal) NIPS 2016
- [Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks](https://arxiv.org/pdf/1711.10305.pdf) (P3D, decouple spatiotemporal) ICCV 2017
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://arxiv.org/pdf/1711.11248.pdf) (decouple spatiotemporal) CVPR 2018
- [Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification](https://arxiv.org/pdf/1712.04851.pdf) (decouple spatiotemporal) ECCV 2018
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://arxiv.org/pdf/1711.09577.pdf) CVPR 2018

### Orthoganal architecture improvements
- [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://arxiv.org/pdf/1803.02579.pdf)
- [CBAM: Convolutional Block Attention Module](https://arxiv.org/pdf/1807.06521.pdf)


### Reinforcement Learning
- [Playing Atari with Deep Reinforcement Learning](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) NIPS 2013 
- [Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scan](http://comaniciu.net/Papers/MultiscaleDeepReinforcementLearning_PAMI18.pdf)
- [An Artificial Agent for Robust Image Registration](https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14751/14296)

### 3D Perception
- [3D-CNN3D Convolutional Neural Networks for Landing Zone Detection from LiDAR](https://www.ri.cmu.edu/pub_files/2015/3/maturana-root.pdf)
- [Generative and Discriminative Voxel Modeling with Convolutional Neural Networks](https://arxiv.org/pdf/1608.04236.pdf)
- [Orientation-boosted Voxel Nets for 3D Object Recognition](https://arxiv.org/pdf/1604.03351.pdf) (ORION) 
- [GIFT: A Real-time and Scalable 3D Shape Search Engine](https://arxiv.org/pdf/1604.01879.pdf) CVPR 2016
- [3D Shape Segmentation with Projective Convolutional Networks](https://people.cs.umass.edu/~kalo/papers/shapepfcn/) (ShapePFCN)CVPR 2017
- [Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networks](https://arxiv.org/pdf/1706.04496.pdf)
- [Open3D: A Modern Library for 3D Data Processing](http://www.open3d.org/wordpress/wp-content/paper.pdf)
- [Multimodal Deep Learning for Robust RGB-D Object Recognition](https://arxiv.org/pdf/1507.06821.pdf) IROS 2015
- [FlowNet3D: Learning Scene Flow in 3D Point Clouds](https://arxiv.org/pdf/1806.01411.pdf) CVPR 2019
- [Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling](https://arxiv.org/pdf/1712.06760.pdf) CVPR 2018 (Neighbors Do Help: Deeply Exploiting Local Structures of Point Clouds)
- [PU-Net: Point Cloud Upsampling Network](https://arxiv.org/pdf/1801.06761.pdf) CVPR 2018
- [Recurrent Slice Networks for 3D Segmentation of Point Clouds](https://arxiv.org/pdf/1802.04402.pdf) CVPR 2018
- [SPLATNet: Sparse Lattice Networks for Point Cloud Processing](https://arxiv.org/pdf/1802.08275.pdf) CVPR 2018
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://arxiv.org/pdf/1606.09375.pdf) NIPS 2016
- [Semi-Supervised Classification with Graph Convolutional Networks](https://arxiv.org/pdf/1609.02907.pdf) ICLR 2017
- [Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks](https://arxiv.org/pdf/1704.06803.pdf) NIPS 2017
- [Graph Attention Networks](https://arxiv.org/pdf/1710.10903.pdf) ICLR 2018
- [3D-SSD: Learning Hierarchical Features from RGB-D Images for Amodal 3D Object Detection](https://arxiv.org/pdf/1711.00238.pdf) (3D SSD)
- [Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models](https://arxiv.org/pdf/1704.01222.pdf) ICCV 2017
- [Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis](https://arxiv.org/pdf/1612.00101.pdf) CVPR 2017
- [IPOD: Intensive Point-based Object Detector for Point Cloud](https://arxiv.org/pdf/1812.05276.pdf)
- [Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes from 2D Ones in RGB-Depth Images](https://cis.temple.edu/~latecki/Papers/DengCVPR2017.pdf) CVPR 2017
- [2D-Driven 3D Object Detection in RGB-D Images](http://openaccess.thecvf.com/content_ICCV_2017/papers/Lahoud_2D-Driven_3D_Object_ICCV_2017_paper.pdf)
- [3D-SSD: Learning Hierarchical Features from RGB-D Images for Amodal 3D Object Detection](https://arxiv.org/pdf/1711.00238.pdf)


### NLP
- [A Convolutional Neural Network for Modelling Sentences](https://arxiv.org/pdf/1404.2188.pdf) ACL 2014
- [FastText: Bag of Tricks for Efficient Text Classification](https://arxiv.org/pdf/1607.01759.pdf) ACL 2017
- [Siamese recurrent architectures for learning sentence similarity](https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/12195/12023) AAAI 2016
- [Efficient Estimation of Word Representations in Vector Space](https://arxiv.org/pdf/1301.3781.pdf) ICLR 2013
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://arxiv.org/pdf/1409.0473.pdf) ICLR 2015
- [Transformers: Attention Is All You Need](https://arxiv.org/pdf/1706.03762.pdf) NIPS 2017

### Unsorted
- [Learning Depth with Convolutional Spatial Propagation Network](https://arxiv.org/abs/1810.02695) (Baidu, depth from SPN) ECCV 2018
- [Model Vulnerability to Distributional Shifts over Image Transformation Sets](https://arxiv.org/pdf/1903.11900.pdf) (CVPR workshop) [tl:dr](https://www.reddit.com/r/MachineLearning/comments/b81uwq/r_model_vulnerability_to_distributional_shifts/)
- [A Unified Panoptic Segmentation Network](https://arxiv.org/pdf/1901.03784.pdf) CVPR 2019 PanSeg
- [FastDraw: Addressing the Long Tail of Lane Detection by Adapting a Sequential Prediction Network](https://arxiv.org/abs/1905.04354)
- [PSMNet: Pyramid Stereo Matching Network](https://arxiv.org/pdf/1803.08669.pdf) CVPR 2018
- [ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst](https://arxiv.org/pdf/1812.03079.pdf) (Waymo)
- [Stereo R-CNN based 3D Object Detection for Autonomous Driving](https://arxiv.org/pdf/1902.09738.pdf) CVPR 2019
- [Deep Rigid Instance Scene Flow](https://people.csail.mit.edu/weichium/papers/cvpr19-dsisf/paper.pdf) CVPR 2019
- [GeoNet: Deep Geodesic Networks for Point Cloud Analysis](https://arxiv.org/pdf/1901.00680.pdf) CVPR 2019 (oral, Megvii)
- [StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation](http://www.bmva.org/bmvc/2015/papers/paper109/paper109.pdf)
- [DenseBox: Unifying Landmark Localization with End to End Object Detection](https://arxiv.org/pdf/1509.04874.pdf)
- [Calibration of Heterogeneous Sensor Systems](https://arxiv.org/pdf/1812.11445.pdf)
- [nuScenes: A multimodal dataset for autonomous driving](https://arxiv.org/pdf/1903.11027.pdf) (dataset, point cloud, radar)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://arxiv.org/pdf/1610.02357.pdf) (Xception)
- [IntroSensor Fusion for Adas  (from )](https://zhuanlan.zhihu.com/p/40967227) (Up to CVPR 2018)
- [Deep Hough Voting for 3D Object Detection in Point Clouds](https://arxiv.org/pdf/1904.09664.pdf) (from Charles Qi)
- [Efficient Deep Learning Inference based on Model Compression](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w33/Zhang_Efficient_Deep_Learning_CVPR_2018_paper.pdf) (Model Compression)
- [FCNN: Fourier Convolutional Neural Networks](http://ecmlpkdd2017.ijs.si/papers/paperID11.pdf) (FFT as CNN)
- [Visualizing the Loss Landscape of Neural Nets](https://papers.nips.cc/paper/7875-visualizing-the-loss-landscape-of-neural-nets.pdf) NIPS 2018
- [Automatic adaptation of object detectors to new domains using self-training](https://arxiv.org/pdf/1904.07305.pdf) CVPR 2019 (find corner case and boost)
- [Missing Labels in Object Detection](http://openaccess.thecvf.com/content_CVPRW_2019/papers/Weakly%20Supervised%20Learning%20for%20Real-World%20Computer%20Vision%20Applications/Xu_Missing_Labels_in_Object_Detection_CVPRW_2019_paper.pdf) CVPR 2019
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://arxiv.org/abs/1705.07115) (uncertainty)
- [Learning to Drive from Simulation without Real World Labels](https://arxiv.org/abs/1812.03823) ICRA 2019 (domain adaptation, sim2real)
- [Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints](https://arxiv.org/pdf/1802.05522.pdf) CVPR 2018
- [YUVMultiNet: Real-time YUV multi-task CNN for autonomous driving](https://arxiv.org/pdf/1904.05673.pdf) CVPR 2019 (Real Time, Low Power)
- [Polar Transformer Networks](https://arxiv.org/abs/1709.01889) ICLR 2018
- [Circular Object Detection in Polar Coordinates for 2D LIDAR Data](https://www.researchgate.net/publication/309365539_Circular_Object_Detection_in_Polar_Coordinates_for_2D_LIDAR_DataCCPR2016) CCPR 2016
- [Adaptive Scheduling for Multi-Task Learning](https://marcpickett.com/cl2018/CL-2018_paper_82.pdf) NIPS 2018 (NMT)
- [A Novel Approach for Detecting Road Based on Two-Stream Fusion Fully Convolutional Network](https://ieeexplore.ieee.org/document/8500551/) (convert camera to BEV)
- [In-Place Activated BatchNorm for Memory-Optimized Training of DNNs](https://arxiv.org/abs/1712.02616) CVPR 2018 (optimized BatchNorm + ReLU)
- [Deep Fusion of Heterogeneous Sensor Modalities for the Advancements of ADAS to Autonomous Vehicles](http://sci-hub.tw/10.1109/VLSI-DAT.2018.8373245)
- [Revisiting Small Batch Training for Deep Neural Networks](https://arxiv.org/abs/1804.07612)
- [ICML2019 workshop: Adaptive and Multitask Learning: Algorithms & Systems](https://icml.cc/Conferences/2019/ScheduleMultitrack?event=3504) ICML 2019

### Datasets and Surveys
- [The DriveU Traffic Light Dataset: Introduction and Comparison with Existing Datasets](https://ieeexplore.ieee.org/document/8460737) ICRA 2018 
- [The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset](https://arxiv.org/abs/1909.01300)
- [Vision for Looking at Traffic Lights: Issues, Survey, and Perspectives](http://cvrr.ucsd.edu/publications/2016/trafficSignalsITSTrans2016.pdf) (traffic light survey, UCSD LISA)
- [Review of Graph Spectrum Theory](paper_notes/graph_spectrum.md) (WIP)
- [3D Deep Learning Tutorial at CVPR 2017](https://www.youtube.com/watch?v=8CenT_4HWyY) [[Notes](paper_notes/3ddl_cvpr2017.md)] - (WIP)
- [A Survey on Neural Architecture Search](https://arxiv.org/pdf/1905.01392.pdf)
- [Network pruning tutorial](https://jacobgil.github.io/deeplearning/pruning-deep-learning) (blog)
- [GNN tutorial at CVPR 2019](https://xiaolonw.github.io/graphnn/)

### Unsupervised depth estimation
- [Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation](https://arxiv.org/pdf/1808.00769.pdf) 3DV 2018
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://arxiv.org/pdf/1406.2283.pdf) NIPS 2014 (Eigen et al)
- [Learning Depth from Monocular Videos using Direct Methods](https://arxiv.org/abs/1712.00175) CVPR 2018 (monocular depth estimation)
- [Virtual-Normal: Enforcing geometric constraints of virtual normal for depth prediction](https://arxiv.org/abs/1907.12209) [[Notes](paper_notes/virtual_normal.md)] ICCV 2019 (better generation of PL)
- [Exploiting temporal consistency for real-time video depth estimation](https://arxiv.org/abs/1908.03706) ICCV 2019 
- [Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera](https://arxiv.org/abs/1907.05820) ICCV 2019
- [Spatial Correspondence with Generative Adversarial Network: Learning Depth from Monocular Videos](http://openaccess.thecvf.com/content_ICCV_2019/papers/Wu_Spatial_Correspondence_With_Generative_Adversarial_Network_Learning_Depth_From_Monocular_ICCV_2019_paper.pdf) ICCV 2019
- [Unsupervised Collaborative Learning of Keyframe Detection and Visual Odometry Towards Monocular Deep SLAM](http://openaccess.thecvf.com/content_ICCV_2019/papers/Sheng_Unsupervised_Collaborative_Learning_of_Keyframe_Detection_and_Visual_Odometry_Towards_ICCV_2019_paper.pdf) ICCV 2019
- [Visualization of Convolutional Neural Networks for Monocular Depth Estimation](https://arxiv.org/abs/1904.03380) ICCV 2019
- [Self-Supervised Monocular Depth Hints](https://arxiv.org/abs/1909.09051) ICCV 2019


### 3DOD with lidar
- [PIXOR: Real-time 3D Object Detection from Point Clouds](https://arxiv.org/pdf/1902.06326.pdf) CVPR 2018 (birds eye view)
- [PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation](https://arxiv.org/pdf/1807.00652.pdf) (pointnet alternative, backbone)
- [Vehicle Detection from 3D Lidar Using Fully Convolutional Network](https://arxiv.org/pdf/1608.07916.pdf) (VeloFCN) RSS 2016 
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://arxiv.org/abs/1904.08889) (from the authors of PointNet)
- [PointCNN: Convolution On X-Transformed Points](https://arxiv.org/pdf/1801.07791.pdf) NIPS 2018
- [L3-Net: Towards Learning based LiDAR Localization for Autonomous Driving](https://songshiyu01.github.io/pdf/L3Net_W.Lu_Y.Zhou_S.Song_CVPR2019.pdf) CVPR 2019
- [RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement](https://arxiv.org/abs/1811.03818) (sensor fusion, 3D mono proposal, refined in point cloud)
- [DeLS-3D: Deep Localization and Segmentation with a 3D Semantic Map](https://arxiv.org/pdf/1805.04949.pdf) CVPR 2018


## To be sorted
- [Classification of Objects in Polarimetric Radar Images Using CNNs at 77 GHz](http://sci-hub.tw/10.1109/APMC.2017.8251453) (Radar, polar) <-- todo
- [Gated2Depth: Real-time Dense Lidar from Gated Images](https://arxiv.org/abs/1902.04997) ICCV 2019 oral
- [PifPaf: Composite Fields for Human Pose Estimation](https://arxiv.org/abs/1903.06593) CVPR 2019
- [Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Raining](https://arxiv.org/abs/1906.11129) CVPR 2019
- [Hybrid Task Cascade for Instance Segmentation](https://arxiv.org/abs/1901.07518) CVPR 2019 (cascaded mask RCNN)
- [3DOP: 3D Object Proposals for Accurate Object Class Detection](https://papers.nips.cc/paper/5644-3d-object-proposals-for-accurate-object-class-detection) NIPS 2015
- [DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation](https://arxiv.org/abs/1904.10097)
- [Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360 Panoramic Imagery](https://arxiv.org/abs/1808.06253) ECCV 2018 (Monocular 3D object detection and depth estimation)
- [Measuring Calibration in Deep Learning](https://arxiv.org/abs/1904.01685) CVPR 2019
- [Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation](https://arxiv.org/abs/1908.00598) ICCV 2019 (epistemic uncertainty)
- [Deep Learning Based 3D Object Detection for Automotive Radar and Camera](https://www.astyx.com/fileadmin/redakteur/dokumente/Deep_Learning_Based_3D_Object_Detection_for_Automotive_Radar_and_Camera.PDF) (Astyx)
- [End-to-end Lane Detection through Differentiable Least-Squares Fitting](https://arxiv.org/abs/1902.00293) ICCV 2019
- [Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection](https://arxiv.org/abs/1903.01864) IROS 2019
- [Dropout Sampling for Robust Object Detection in Open-Set Conditions](https://arxiv.org/abs/1710.06677) ICRA 2018 (Niko Snderhauf)
- [Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection](https://arxiv.org/abs/1809.06006) ICRA 2019 (Niko Snderhauf)
- [GCNet: End-to-End Learning of Geometry and Context for Deep Stereo Regression](https://arxiv.org/abs/1703.04309) ICCV 2017 (disparity estimation)
- [PSMNet: Pyramid Stereo Matching Network](https://arxiv.org/abs/1803.08669) CVPR 2018 (disparity estimation)
- [Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching](https://arxiv.org/abs/1806.01677) NIPS 2018 (disparity estimation)
- [Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera](https://arxiv.org/abs/1807.00275) ICRA 2019/kbd>
- [Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation](https://arxiv.org/abs/1904.00415) ICCV 2019 (radar)
- [Towards Scene Understanding: Unsupervised Monocular Depth Estimation with Semantic-aware Representation](http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Towards_Scene_Understanding_Unsupervised_Monocular_Depth_Estimation_With_Semantic-Aware_Representation_CVPR_2019_paper.pdf) CVPR 2019
- [DDP: Dense Depth Posterior from Single Image and Sparse Range](https://arxiv.org/abs/1901.10034) CVPR 2019
- [End-to-End Learning of Geometry and Context for Deep Stereo Regression](https://arxiv.org/abs/1703.04309) ICCV 2017
- [Understanding deep learning requires rethinking generalization](https://arxiv.org/abs/1611.03530) ICLR 2017 (best paper)
- [PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation](https://arxiv.org/pdf/1711.10871.pdf) CVPR 2018 (sensor fusion)
- [CNNs for Interference Mitigation and Denoising in Automotive Radar Using Real-World Data](https://ml4ad.github.io/files/papers/CNNs%20for%20Interference%20Mitigation%20and%20Denoising%20in%20Automotive%20Radar%20Using%20Real-World%20Data.pdf) NeurIPS 2019 (radar)
- [A Multi-Sensor Fusion System for Moving Object Detection and Tracking in Urban Driving Environments](http://www.cs.cmu.edu/~youngwoo/doc/icra-14-sensor-fusion.pdf) ICRA 2014
- [Domain Adaptive Faster R-CNN for Object Detection in the Wild](https://arxiv.org/abs/1803.03243) CVPR 2018
- [Foggy Cityscapes: Semantic Foggy Scene Understanding with Synthetic Data](https://arxiv.org/abs/1708.07819) IJCV 2018
- [Foggy Cityscapes ECCV: Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding](https://arxiv.org/abs/1808.01265) ECCV 2018
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://arxiv.org/abs/1610.02136) ICLR 2017 (NLL score as anomaly score)
- [Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes](https://arxiv.org/abs/1708.01566) IJCV 2018 (data augmentation with AR, Toyota)


## Technical Debt
- CRF
- [Visual SLAM and Visual Odometry](https://link.springer.com/content/pdf/10.1007%2Fs40903-015-0032-7.pdf)
- ORB SLAM
- ORB (ICCV 2011)
- Bundle Adjustment
- 3D vision
- Codebase of STN
- Codebase of monodepth
- Codebase of KITTI devkit
- [SLAM/VIO](https://zhuanlan.zhihu.com/p/34995102)

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