The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
Camouflaged Object Segmentation with Distraction Mining
Open accessAbstract
Camouflaged object segmentation (COS) aims to identify objects that are "perfectly" assimilate into their surroundings, which has a wide range of valuable applications. The key challenge of COS is that there exist high intrinsic similarities between the candidate objects and noise background. In this paper, we strive to embrace challenges towards effective and efficient COS. To this end, we develop a bio-inspired framework, termed Positioning and Focus Network (PFNet), which mimics the process of predation in nature. Specifically, our PFNet contains two key modules, i.e., the positioning module (PM) and the focus module (FM). The PM is designed to mimic the detection process in predation for positioning the potential target objects from a global perspective and the FM is then used to perform the identification process in predation for progressively refining the coarse prediction via focusing on the ambiguous regions. Notably, in the FM, we develop a novel distraction mining strategy for the distraction discovery and removal, to benefit the performance of estimation. Extensive experiments demonstrate that our PFNet runs in real-time (72 FPS) and significantly outperforms 18 cutting-edge models on three challenging datasets under four standard metrics.
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Cites
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
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Feature Pyramid Networks for Object Detection
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CBAM: Convolutional Block Attention Module
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Cited by
Simultaneously Localize, Segment and Rank the Camouflaged Objects
Lv, Y., Zhang, J., Dai, Y., Li, A., Liu, B., Barnes, N., & Fan, D.-P. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01142 · Cited by 584
Self-citationCamouflaged Object Detection with Feature Decomposition and Edge Reconstruction
He, C., Li, K., Zhang, Y., Tang, L., Zhang, Y., Guo, Z., & Li, X. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.02111 · Cited by 382
Boundary-Guided Camouflaged Object Detection
Sun, Y., Wang, S., Chen, C., & Xiang, T.-Z. (2022) · Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/186 · Cited by 336
Detecting Camouflaged Object in Frequency Domain
Zhong, Y., Li, B., Tang, L., Kuang, S., Wu, S., & Ding, S. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00446 · Cited by 317
Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformers
Huang, Z., Dai, H., Xiang, T.-Z., Wang, S., Chen, H.-X., Qin, J., & Xiong, H. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00538 · Cited by 292
Metadata
- Authors
- Mei, H., Ji, G.-P., Wei, Z., Yang, X., Wei, X., & Fan, D.-P.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 8768-8777
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3168112135
- Cited by
- 565
- References
- 72
- FWCI
- 28.42
- Type
- conference paper
- Language
- en
- Open access
- green
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- Mei, H., Ji, G.-P., Wei, Z., Yang, X., Wei, X., & Fan, D.-P. (2021). Camouflaged Object Segmentation with Distraction Mining. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr46437.2021.00866
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