The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Camouflaged Object Detection
Abstract
We present a comprehensive study on a new task named camouflaged object detection (COD), which aims to identify objects that are “seamlessly” embedded in their surroundings. The high intrinsic similarities between the target object and the background make COD far more challenging than the traditional object detection task. To address this issue, we elaborately collect a novel dataset, called COD10K, which comprises 10,000 images covering camouflaged objects in various natural scenes, over 78 object categories. All the images are densely annotated with category, bounding-box, object-/instance-level, and matting-level labels. This dataset could serve as a catalyst for progressing many vision tasks, e.g., localization, segmentation, and alpha-matting, etc. In addition, we develop a simple but effective framework for COD, termed Search Identification Network (SINet). Without any bells and whistles, SINet outperforms various state-of-the-art object detection baselines on all datasets tested, making it a robust, general framework that can help facilitate future research in COD. Finally, we conduct a large-scale COD study, evaluating 13 cutting-edge models, providing some interesting findings, and showing several potential applications. Our research offers the community an opportunity to explore more in this new field. The code will be available at https://github.com/DengPingFan/SINet/.
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Cites
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Self-citationUC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
Zhang, J., Fan, D.-P., Dai, Y., Anwar, S., Saleh, F. S., Zhang, T., & Barnes, N. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00861 · Cited by 423
Self-citationRGB-D salient object detection: A survey
Zhou, T., Fan, D.-P., Cheng, M.-M., Shen, J., & Shao, L. (2021) · Computational Visual Media · doi:10.1007/s41095-020-0199-z · Cited by 331
Self-citationSAM struggles in concealed scenes — empirical study on “Segment Anything”
Ji, G.-P., Fan, D.-P., Xu, P., Zhou, B., Cheng, M.-M., & Van Gool, L. (2023) · Science China Information Sciences · doi:10.1007/s11432-023-3881-x · Cited by 101
Self-citationAbdar, M., Khosravi, A., Islam, S. M. S., Acharya, U. R., & Vasilakos, A. V. (2022) · IEEE Systems, Man, and Cybernetics Magazine · doi:10.1109/msmc.2022.3150144 · Cited by 55
Metadata
- Authors
- Fan, D.-P., Ji, G.-P., Sun, G., Cheng, M.-M., Shen, J., & Shao, L.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 2774-2784
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3034684132
- Cited by
- 896
- References
- 15
- FWCI
- 23.76
- Type
- conference paper
- Language
- en
- Open access
- closed
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