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Segment anything model (SAM), as the name suggests, is claimed to be capable of cutting out any object and demonstrates impressive zero-shot transfer performance with the guidance of prompts.
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2023
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2023
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C. Zhang, Y. Qiao, S. Tariq, S. Zheng, C. Zhang, C. Li, H. Shin, and C. S. Hong, “Understanding segment anything model: Sam is biased towards texture rather than shape,” 2023
2023
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Z. Zhu, Y. Zhang, H. Chen, Y. Dong, S. Zhao, W. Ding, J. Zhong, and S. Zheng, “Understanding the robustness of 3d object detection with bird’s-eye-view representations in autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 21 600–21 610
2023
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L. Ke, Y.-W. Tai, and C.-K. Tang, “Occlusion-aware video object inpainting,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 14 468–14 478
2021
Cited alongside, same era.
2021
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2021
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2021
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2021
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W. Wang, B. Yin, T. Yao, L. Zhang, Y. Fu, S. Ding, J. Li, F. Huang, and X. Xue, “Delving into data: Effectively substitute training for black-box attack,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 4761–4770
2021
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2021
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2022
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2023
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J. Ma and B. Wang, “Segment anything in medical images,” arXiv preprint arXiv:2304.12306 , 2023
2023
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2023
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D. Han, C. Zhang, Y. Qiao, M. Qamar, Y. Jung, S. Lee, S.-H. Bae, and C. S. Hong, “Segment anything model (sam) meets glass: Mirror and transparent objects cannot be easily detected,” arXiv preprint , 2023
2023
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IDEA-Research, “Grounded segment anything,” 2023, gitHub repository. [Online]. Available: https://github.com/IDEA-Research/Grounded-Segment-Anything
2023
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J. Chen, Z. Yang, and L. Zhang, “Semantic-segment-anything,” 2023, gitHub repository. [Online]. Available: https://github.com/fudan-zvg/Semantic-Segment-Anything
2023
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C. Park, “segment anything with clip,” 2023, gitHub repository. [Online]. Available: https://github.com/Curt-Park/segment-anything-with-clip
2023
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Zxyang, “Segment and track anything,” 2023, gitHub repository. [Online]. Available: https://github.com/z-x-yang/Segment-and-Track-Anything
2023
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Y. Qiao, M. S. Munir, A. Adhikary, A. D. Raha, S. H. Hong, and C. S. Hong, “A framework for multi-prototype based federated learning: Towards the edge intelligence,” in 2023 International Conference on Information Networking (ICOIN) . IEEE, 2023, pp. 134–139
2023
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C. Zhang, C. Zhang, T. Kang, D. Kim, S.-H. Bae, and I. S. Kweon, “Attack-sam: Towards evaluating adversarial robustness of segment anything model,” arXiv preprint , 2023
2023
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J. Zhang, L. Cao, Q. Lai, B. Li, and Y. Qin, “Bifrnet: A brain-inspired feature restoration dnn for partially occluded image recognition,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 12, 2023, pp. 15 296–15 304
2023
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L. Feihong, C. Hang, L. Kang, D. Qiliang, Z. Jian, Z. Kaipeng, and H. Hong, “Toward high-quality face-mask occluded restoration,” ACM Transactions on Multimedia Computing, Communications and Applications , vol. 19, no. 1, pp. 1–23, 2023
2023
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