The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Attention Branch Network: Learning of Attention Mechanism for Visual Explanation
Open accessAbstract
Visual explanation enables humans to understand the decision making of deep convolutional neural network (CNN), but it is insufficient to contribute to improving CNN performance. In this paper, we focus on the attention map for visual explanation, which represents a high response value as the attention location in image recognition. This attention region significantly improves the performance of CNN by introducing an attention mechanism that focuses on a specific region in an image. In this work, we propose Attention Branch Network (ABN), which extends a response-based visual explanation model by introducing a branch structure with an attention mechanism. ABN can be applicable to several image recognition tasks by introducing a branch for the attention mechanism and is trainable for visual explanation and image recognition in an end-to-end manner. We evaluate ABN on several image recognition tasks such as image classification, fine-grained recognition, and multiple facial attribute recognition. Experimental results indicate that ABN outperforms the baseline models on these image recognition tasks while generating an attention map for visual explanation. Our code is available.
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Cites
Deep Residual Learning for Image Recognition
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ImageNet classification with deep convolutional neural networks
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Squeeze-and-Excitation Networks
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
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Cited by
Attention, please! A survey of neural attention models in deep learning
de Santana Correia, A., & Colombini, E. L. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10148-x · Cited by 298
Towards Visually Explaining Variational Autoencoders
Liu, W., Li, R., Zheng, M., Karanam, S., Wu, Z., Bhanu, B., Radke, R. J., & Camps, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00867 · Cited by 294
Joo, S., Ko, E. S., Kwon, S., Jeon, E., Jung, H., Kim, J.-Y., Chung, M. J., & Im, Y.-H. (2021) · Scientific Reports · doi:10.1038/s41598-021-98408-8 · Cited by 137
Metadata
- Authors
- Fukui, H., Hirakawa, T., Yamashita, T., & Fujiyoshi, H.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 10697-10706
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963811535
- Cited by
- 515
- References
- 21
- FWCI
- 22.94
- Type
- conference paper
- Language
- en
- Open access
- green
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