The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Simultaneously Localize, Segment and Rank the Camouflaged Objects
Open accessAbstract
Camouflage is a key defence mechanism across species that is critical to survival. Common strategies for camouflage include background matching, imitating the color and pattern of the environment, and disruptive coloration, disguising body outlines [37]. Camouflaged object detection (COD) aims to segment camouflaged objects hiding in their surroundings. Existing COD models are built upon binary ground truth to segment the camouflaged objects without illustrating the level of camouflage. In this paper, we revisit this task and argue that explicitly modeling the conspicuousness of camouflaged objects against their particular backgrounds can not only lead to a better understanding about camouflage and evolution of animals, but also provide guidance to design more sophisticated camouflage techniques. Furthermore, we observe that it is some specific parts of the camouflaged objects that make them detectable by predators. With the above understanding about camouflaged objects, we present the first ranking based COD network (Rank-Net) to simultaneously localize, segment and rank camouflaged objects. The localization model is proposed to find the discriminative regions that make the camouflaged object obvious. The segmentation model segments the full scope of the camouflaged objects. Further, the ranking model infers the detectability of different camouflaged objects. Moreover, we contribute a large COD testing set to evaluate the generalization ability of COD models. Experimental results show that our model achieves new state-of-the-art, leading to a more interpretable COD network1.
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Cites
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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
Feature Pyramid Networks for Object Detection
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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
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Dual Attention Network for Scene Segmentation
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Cited by
Camouflaged 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
Deep Gradient Learning for Efficient Camouflaged Object Detection
Ji, G.-P., Fan, D.-P., Chou, Y.-C., Dai, D., Liniger, A., & Van Gool, L. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1365-9 · Cited by 284
Self-citation
Metadata
- Authors
- Lv, Y., Zhang, J., Dai, Y., Li, A., Liu, B., Barnes, N., & Fan, D.-P.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 11586-11596
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3176152216
- Cited by
- 584
- References
- 63
- FWCI
- 25.47
- Type
- conference paper
- Language
- en
- Open access
- green
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