@inproceedings{
chen2024holisticcam,
title={Holistic-{CAM}: Ultra-lucid and Sanity Preserving Visual Interpretation in Holistic Stage of {CNN}s},
author={Pengxu Chen and Huazhong Liu and Jihong Ding and Jiawen Luo and Peng Tan and Laurence T. Yang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=O9Vuj6lzya}
}
As the visual interpretations for convolutional neural networks (CNNs), backpropagation attribution methods have been garnering growing attention. Nevertheless, majority of those methods merely concentrate on the ultimate convolutional layer, leading to tiny and concentrated interpretations that fail to adequately clarify the model-central attention. Therefore, we propose a precise attribution method (i. e., Holistic-CAM) for high-definition visual interpretation in the holistic stage of CNNs. Specifically, we first present weighted positive gradients to guarantee the sanity of interpretations in shallow layers and leverage multi-scale fusion to improve the resolution across the holistic stage. Then, we further propose fundamental scale denoising to eliminate the faithless attribution originated from fusing larger-scale components. The proposed method is capable of simultaneously rendering fine-grained and faithful attribution for CNNs from shallow to deep layers. Extensive experimental results demonstrate that Holistic-CAM outperforms state-of-the-art methods on common-used benchmarks, including deletion and insertion, energy-based point game as well as remove and debias on ImageNet-1k, it also passes the sanity check easily.
Holistic-CAM: Ultra-lucid and Sanity Preserving Visual Interpretation in Holistic Stage of CNNs
Pengxu Chen Huazhong Liu Jihong Ding Jiawen Luo Peng Tan Laurence T. Yang