This is the official pytorch implementation of the paper:
to be presented at BMVC 2020 (Oral presentation),
Authors:
Ruigang Fu,
Qingyong Hu
Xiaohu Dong,
Yulan Guo,
Yinghui Gao and
Biao Li,
XGrad-CAM is a CNN visualization method, try to explain why classification CNNs predict what they predict. It is class-discriminative, efficient and able to highlight the regions belonging to the objects of interest.
The main difference between XGrad-CAM and Grad-CAM locates at line 116 - line120:
X_weights = np.sum(grads_val[0, :] * target, axis=(1, 2))
X_weights = X_weights / (np.sum(target, axis=(1, 2)) + 1e-6)
weights = np.mean(grads_val, axis=(2, 3))[0, :]
Usage: python XGrad-cam.py --image-path <path_to_image> --target-index <class_of_interest> for CPU computation, add --use-cuda for GPU acceleration.
Example: python XGrad-CAM.py --image-path ./examples/ILSVRC2012_val_00000077.JPEG --target-index 159 --use-cuda
Output: class of interest: n02087394 Rhodesian ridgeback
Results:
left is Grad-CAM, right is XGrad-CAM
This is a simple script of experimental proof for our statement that given an arbitrary layer in ReLU-CNNs, there exists a specific equation between the class score and the feature maps of the layer (Eq.(5) in our paper).
Usage: python Proof_verify.py --image-path <path_to_image> --target-index <class_of_interest>
For any class of interest, the result will show that class_score-gradients*feature-bias_term=0
These codes are based on https://github.com/jacobgil/pytorch-grad-cam. Thanks to the author Jacob Gildenblat for the beautiful original code.
If you find our work useful in your research, please consider citing:
@inproceedings{fu2020axiom,
title={Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs},
author={Fu, Ruigang and Hu, Qingyong and Dong, Xiaohu and Guo, Yulan and Gao, Yinghui and Li, Biao},
booktitle={British Machine Vision Conference},
year={2020}
}