The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Self-Supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation
Open accessAbstract
Image-level weakly supervised semantic segmentation is a challenging problem that has been deeply studied in recent years. Most of advanced solutions exploit class activation map (CAM). However, CAMs can hardly serve as the object mask due to the gap between full and weak supervisions. In this paper, we propose a self-supervised equivariant attention mechanism (SEAM) to discover additional supervision and narrow the gap. Our method is based on the observation that equivariance is an implicit constraint in fully supervised semantic segmentation, whose pixel-level labels take the same spatial transformation as the input images during data augmentation. However, this constraint is lost on the CAMs trained by image-level supervision. Therefore, we propose consistency regularization on predicted CAMs from various transformed images to provide self-supervision for network learning. Moreover, we propose a pixel correlation module (PCM), which exploits context appearance information and refines the prediction of current pixel by its similar neighbors, leading to further improvement on CAMs consistency. Extensive experiments on PASCAL VOC 2012 dataset demonstrate our method outperforms state-of-the-art methods using the same level of supervision. The code is released online.
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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Fully convolutional networks for semantic segmentation
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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
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Research works printed but not linked: 1
- hou, 0, Self-erasing network for integral object attention, Proc Neural Information Processing Systems (NIPS)
Cited by
Han (2023) · doi:10.1109/tpami.2022.3152247 · Cited by 4,682
Causal Intervention for Weakly-Supervised Semantic Segmentation
Zhang, D., Zhang, H., Tang, J., Hua, X., & Sun, Q. (2020) · arXiv · doi:10.48550/arxiv.2009.12547 · Cited by 353
Scaling Open-Vocabulary Image Segmentation with Image-Level Labels
Ghiasi, G., Gu, X., Cui, Y., & Lin, T.-Y. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20059-5_31 · Cited by 339
SatMAE: Pre-Training Transformers for Temporal and Multi-Spectral Satellite Imagery
Cong, Y., Khanna, S., Meng, C., Liu, P., Rozi, E., He, Y., Burke, M., Lobell, D., & Ermon, S. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-0015 · Cited by 303
Ru, L., Zhan, Y., Yu, B., & Du, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01634 · Cited by 281
Metadata
- Authors
- Wang, Y., Zhang, J., Kan, M., Shan, S., & Chen, X.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 12272-12281
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3034930876
- Cited by
- 770
- References
- 42
- FWCI
- 33.66
- Type
- conference paper
- Language
- en
- Open access
- green
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- Wang, Y., Zhang, J., Kan, M., Shan, S., & Chen, X. (2020). Self-Supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr42600.2020.01229
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