https://github.com/imageomics/finer-cam
This is an official implementation for Finer-CAM: Spotting the Difference Reveals Finer Details for Visual Explanation. [CVPR'25]
https://github.com/imageomics/finer-cam
class-activation-maps computer-vision explainable-ai fine-grained-classification saliency-maps vision-transformers visualizations xai
Last synced: 26 days ago
JSON representation
This is an official implementation for Finer-CAM: Spotting the Difference Reveals Finer Details for Visual Explanation. [CVPR'25]
- Host: GitHub
- URL: https://github.com/imageomics/finer-cam
- Owner: Imageomics
- License: apache-2.0
- Created: 2025-01-21T00:48:10.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-11-10T03:53:32.000Z (3 months ago)
- Last Synced: 2025-11-10T05:33:14.224Z (3 months ago)
- Topics: class-activation-maps, computer-vision, explainable-ai, fine-grained-classification, saliency-maps, vision-transformers, visualizations, xai
- Language: Jupyter Notebook
- Homepage:
- Size: 5.53 MB
- Stars: 43
- Watchers: 3
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
- Citation: CITATION.cff
Awesome Lists containing this project
README
# Finer-CAM : Spotting the Difference Reveals Finer Details for Visual Explanation [CVPR 2025]
Official implementation of "Finer-CAM [[arxiv]](https://arxiv.org/pdf/2501.11309)".
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](https://github.com/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: [](https://colab.research.google.com/drive/1plLrL7vszVD5r71RGX3YOEXEBmITkT90?usp=sharing)
- Test the **classifier** demo to explore class-specific activation maps with enhanced explainability: [](https://colab.research.google.com/drive/1SAjRTmGy31G-GjtAc9pVH6isPjm1hWsj?usp=sharing)
## 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
2. **Unzip the downloaded file**
```bash
unzip datasets/stanford_cars.zip -d datasets/
3. 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 \
--dataset_path \
--save_path
```
### 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 \
--cams_path \
--save_path
```
## Acknowledgement
We utilized code from:
- [pytorch_grad_cam](https://github.com/jacobgil/pytorch-grad-cam/tree/61e9babae8600351b02b6e90864e4807f44f2d4a)
- [clip-es](https://github.com/linyq2117/CLIP-ES)
Thanks for their wonderful works.
# Citation [](https://arxiv.org/abs/2501.11309)
If you find this repository useful, please consider citing our work :pencil: and giving a star :star2: :
```
@InProceedings{zhang2025finer,
author = {Zhang, Ziheng and Gu, Jianyang and Chowdhury, Arpita and Mai, Zheda and Carlyn, David and Berger-Wolf, Tanya and Su, Yu and Chao, Wei-Lun},
title = {Finer-CAM: Spotting the Difference Reveals Finer Details for Visual Explanation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9611-9620}
}
```