Multimodal Color recommendation in vector graphic documents

Official implementation of Multimodal Color Recommendation in Vector Graphic Documents, ACM MM 2023.

Introduction

In this study, we propose a multimodal masked color model that integrates both color and textual contexts to provide text-aware color recommendation for graphic documents. Our proposed model comprises self-attention networks to capture the relationships between colors in multiple palettes, and cross-attention networks that incorporate both color and CLIP-based text representations.

This proposal is applicable for two color recommendation tasks, color palette completion, which recommends colors based on the given colors and text, and full palette generation, which generates a complete color palette corresponding to the given text. The code for these two tasks is organized in two separate folders.

Setup

This project has been developed and tested in a Google Cloud Platform (GCP) notebook instance. PS: We use google-cloud-vision to detect image labels. An updated version with Open-source-software(OSS) will be released soon. - Machine type: n1-standard-4 - Environment: TensorFlow Enterprise 2.3 - GPU: NVIDIA T4 * 1. - Python:3.7.12 - CUDA: 11.6

Install pytorch and requirements pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu116 pip3 install requirement.txt

Color palette completion

Task: Recommends colors based on the given colors and text

Target: multiple palettes in a graphic document

Quick demo

colorpalettecompletion/

notebooks/palette_compl.ipynb: recommend colors for multiple palettes in a design - Trained model of color prediction are in data/trained_models/. - Json files for test are pre-created in data/samples/crello_samples/. - New input image files for test are in data/samples/image_samples/.

You can also create a json file for test from crello dataset on a notebook notebooks/createjsonfile.ipynb.

Train models

Step1: Create color and text embedding files of train, validation, and test on a notebook notebooks/preprocess.ipynb - data/data_t2p/color: color corpus of train, validation, and test dataset, and color vocabulary from train dataset - data/data_t2p/text: text contents and image labels of train, validation, and test dataset - data/data_t2p/text/emb_clip_imagemust_seq: pre-created text embedings of text contents and image labels for train, validation, and test

Step2: Train a color model on a notebook notebooks/train_model.ipynb.

Data

data/data_colors/data_colors_labels/.: extracted color palettes for Image-SVG-Text elements, text contents and image lables from Crello-dataset-v2 (the lastest Crello-dataset). - Rawdata in data/crello-dataset-v2: Download and unzip the crello dataset v2 and get .tfrecord files for train/val/test. - Data filter: high frequent image labels, English contents

data/trained_model: trained model for text-aware color completion

data/samples: json sample files for testing the results of color completion

Full palette completion

Task: Generates a complete color palette corresponding to the given text

Target: A single palette for an image

Quick demo

fullpalettegeneration/

Step1: Create color and text embedding files of train, validation, and test on a notebook notebooks/preprocess.ipynb - data/data_t2p/color: color corpus of train, validation, and test dataset, and color vocabulary from train dataset - data/data_t2p/text: text input of train, validation, and test dataset - data/data_t2p/text/emb_clip: pre-created text embedings of text contents and image labels for train, validation, and test

Step2: Generate colors based on given text on a notebook notebooks/palette_gen.ipynb - Trained model of color generation are in data/trained_models/.

Train models

Train a color model on a notebook notebooks/train_model.ipynb.

Data

data/data_colors/.: palette-text pairs for train, validation, and test from Palette-And-Text dataset

data/trained_model: trained model for text-aware palette generation

Citation

bibtex @inproceedings{qiu2023multimodal, title = {Multimodal Color Recommendation in Vector Graphic Documents}, author = {Qiu, Qianru and Wang, Xueting and Otani, Mayu}, booktitle = {Proceedings of the 31st ACM International Conference on Multimedia}, pages = {4003--4011}, year = {2023} }