Finer-CAM : Spotting the Difference Reveals Finer Details for Visual Explanation [CVPR 2025]

Official implementation of "Finer-CAM [arxiv]".

CAM methods highlight image regions influencing predictions but often struggle in fine-grained tasks due to shared feature activation across similar classes. We propose Finer-CAM, which explicitly compares the target class with similar ones, suppressing shared features and emphasizing unique, discriminative details.

Finer-CAM retains CAM’s efficiency, offers precise localization, and adapts to multi-modal zero-shot models, accurately activating object parts or attributes. It enhances explainability in fine-grained tasks without increasing complexity.

Update

2025.3.13: Merged into jacobgil/pytorch-grad-cam, a wonderful library that supports multiple CAM-based methods.

Demo

Experience the power of Finer-CAM with our interactive demos! Witness accurate localization of discriminative features.

• Try the multi-modal demo and see how Finer-CAM activates detailed and relevant regions for diverse concepts:
• Test the classifier demo to explore class-specific activation maps with enhanced explainability:

Reqirements

pip install grad-cam

Preparing Datasets

Stanford Cars

1. Download the dataset using the following command:

```bash curl -L -o datasets/stanford_cars.zip \ https://www.kaggle.com/api/v1/datasets/download/cyizhuo/stanford-cars-by-classes-folder

1. Unzip the downloaded file ```bash unzip datasets/stanford_cars.zip -d datasets/

2. The structure of datasets/should be organized as follows:

datasets/ ├── train/ │ ├── Acura Integra Type R 2001/ │ │ ├── 000405.jpg │ │ ├── 000406.jpg │ │ └── ... │ ├── Acura RL Sedan 2012/ │ │ ├── 000090.jpg │ │ ├── 000091.jpg │ │ └── ... │ └── ... └── test/ ├── Acura Integra Type R 2001/ │ ├── 000450.jpg │ ├── 000451.jpg │ └── ... ├── Acura RL Sedan 2012/ │ ├── 000122.jpg

Usage

Step 1. Generate CAMs for Validation Set

Run the Script:

• Execute the generate_cams.py script with the appropriate arguments using the following command: bash python generate_cams.py \ --classifier_path <path_to_classifier_weight> \ --dataset_path <path_to_dataset_or_image_list> \ --save_path <path_to_save_results>

Step 2. Visualize Results

Run the Script:

• Execute the visualize.py script with the appropriate arguments using the following command: bash python visualize.py --dataset_path <path_to_dataset_directory> \ --cams_path <path_to_cams_directory> \ --save_path <path_to_save_visualizations>

Acknowledgement

We utilized code from:

• pytorchgradcam
  
• clip-es
  

Thanks for their wonderful works.

Citation

If you find this repository useful, please consider citing our work :pencil: and giving a star :star2: : @article{zhang2025finer, title={Finer-CAM: Fine-grained Visual Interpretability through Class-Specific Gradient Refinements}, author={Ziheng Zhang and Jianyang Gu and Arpita Chowdhury and Zheda Mai and David Carlyn and Tanya Berger-Wolf and Yu Su and Wei-Lun Chao}, journal={arXiv preprint arXiv:2501.11309}, year={2025}, }