An implementation of “A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts” (CVPR 2021)
Please download data from here, and unzip "source.zip" to ./source, "result.zip" to ./result. Source data are the images for discovering concepts, and result data are the files that contain concept information. I only implement some basic functions to inference designated images. You can select the image you want to do reasoning from ./result/img2vec/*detect_img. For example, there is a file named ./result/img2vec/fire_engine_detect_img/n03345487_10367_img.png, so the "img_class" is "fire_engine" and "img_idx" is 10367.
You can simply run python Xception_WhyNot.py --img_class fire_engine --img_idx 10367 to do reasoning.
If you use this code for your research, please cite the original paper.
@inproceedings{ge2021peek,
title={A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts},
author={Ge, Yunhao and Xiao, Yao and Xu, Zhi and Zheng, Meng and Karanam, Srikrishna and Chen, Terrence and Itti, Laurent and Wu, Ziyan},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={2195--2204},
year={2021}
}