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Image segmentation plays an important role in vision understanding.
Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: CVPR. pp. 1155–1162 (2013)
2013
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Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: CVPR. pp. 3166–3173 (2013)
2013
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Li, Y., Hou, X., Koch, C., Rehg, J.M., Yuille, A.L.: The secrets of salient object segmentation. In: CVPR. pp. 280–287 (2014)
2014
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Margolin, R., Zelnik-Manor, L., Tal, A.: How to evaluate foreground maps? In: CVPR. pp. 248–255 (2014)
2014
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Silva, J., Histace, A., Romain, O., Dray, X., Granado, B.: Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer. International Journal of Computer Assisted Radiology and Surgery 9
2014
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Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., Vilariño, F.: Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Computerized Medical Imaging and Graphics 43
2015
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Li, G., Yu, Y.: Visual saliency based on multiscale deep features. In: CVPR. pp. 5455–5463 (2015)
2015
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Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI. pp. 234–241. Springer (2015)
2015
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Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automated polyp detection in colonoscopy videos using shape and context information. IEEE Transactions on Medical Imaging 35
2015
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Fan, D.P., Cheng, M.M., Liu, Y., Li, T., Borji, A.: Structure-measure: A new way to evaluate foreground maps. In: ICCV. pp. 4548–4557 (2017)
2017
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Vázquez, D., Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., López, A.M., Romero, A., Drozdzal, M., Courville, A.: A benchmark for endoluminal scene segmentation of colonoscopy images. Journal of Healthcare Engineering 2017
2017
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Wang, L., Lu, H., Wang, Y., Feng, M., Wang, D., Yin, B., Ruan, X.: Learning to detect salient objects with image-level supervision. In: CVPR. pp. 136–145 (2017)
2017
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Liu, S., Huang, D., et al.: Receptive field block net for accurate and fast object detection. In: ECCV. pp. 385–400 (2018)
2018
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Skurowski, P., Abdulameer, H., Błaszczyk, J., Depta, T., Kornacki, A., Kozieł, P.: Animal camouflage analysis: Chameleon database. Unpublished Manuscript 2
2018
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Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., Gelly, S.: Parameter-efficient transfer learning for nlp. In: ICML. pp. 2790–2799. PMLR (2019)
2019
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Le, T.N., Nguyen, T.V., Nie, Z., Tran, M.T., Sugimoto, A.: Anabranch network for camouflaged object segmentation. Computer Vision and Image Understanding 184
2019
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Yang, X., Mei, H., Xu, K., Wei, X., Yin, B., Lau, R.W.: Where is my mirror? In: ICCV. pp. 8809–8818 (2019)
2019
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Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transactions on Medical Imaging 39
2019
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Fan, D.P., Ji, G.P., Sun, G., Cheng, M.M., Shen, J., Shao, L.: Camouflaged object detection. In: CVPR. pp. 2777–2787 (2020)
2020
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Fan, D.P., Ji, G.P., Zhou, T., Chen, G., Fu, H., Shen, J., Shao, L.: Pranet: Parallel reverse attention network for polyp segmentation. In: MICCAI. pp. 263–273. Springer (2020)
2020
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Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., De Lange, T., Johansen, D., Johansen, H.D.: Kvasir-seg: A segmented polyp dataset. In: MMM. pp. 451–462. Springer (2020)
2020
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Lin, J., Wang, G., Lau, R.W.: Progressive mirror detection. In: CVPR. pp. 3697–3705 (2020)
2020
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Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O.R., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection. Pattern Recognition 106
2020
Cited alongside, same era.
Wei, J., Wang, S., Huang, Q.: F 3 net: fusion, feedback and focus for salient object detection. In: AAAI. pp. 12321–12328 (2020)
2020
Cited alongside, same era.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: ICLR (2021)
2023
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He, C., Li, K., Zhang, Y., Tang, L., Zhang, Y., Guo, Z., Li, X.: Camouflaged object detection with feature decomposition and edge reconstruction. In: CVPR. pp. 22046–22055 (2023)
2023
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He, R., Lin, J., Lau, R.W.: Efficient mirror detection via multi-level heterogeneous learning. In: AAAI. vol. 37, pp. 790–798 (2023)
2023
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Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., et al.: Segment anything. In: ICCV. pp. 4015–4026 (2023)
2023
Later among the works it cites.
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2021
Cited alongside, same era.
Fan, D.P., Ji, G.P., Qin, X., Cheng, M.M.: Cognitive vision inspired object segmentation metric and loss function. Scientia Sinica Informationis 6
2021
Cited alongside, same era.
Li, L., Dong, B., Rigall, E., Zhou, T., Dong, J., Chen, G.: Marine animal segmentation. IEEE Transactions on Circuits and Systems for Video Technology 32
2021
Cited alongside, same era.
Lv, Y., Zhang, J., Dai, Y., Li, A., Liu, B., Barnes, N., Fan, D.P.: Simultaneously localize, segment and rank the camouflaged objects. In: CVPR. pp. 11591–11601 (2021)
2021
Cited alongside, same era.
Mei, H., Ji, G.P., Wei, Z., Yang, X., Wei, X., Fan, D.P.: Camouflaged object segmentation with distraction mining. In: CVPR. pp. 8772–8781 (2021)
2021
Cited alongside, same era.
Sun, Y., Chen, G., Zhou, T., Zhang, Y., Liu, N.: Context-aware cross-level fusion network for camouflaged object detection. In: IJCAI. pp. 1025–1031 (2021)
2021
Cited alongside, same era.
Wei, J., Hu, Y., Zhang, R., Li, Z., Zhou, S.K., Cui, S.: Shallow attention network for polyp segmentation. In: MICCAI. pp. 699–708. Springer (2021)
2021
Cited alongside, same era.
Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: ECCVW. pp. 205–218. Springer (2022)
2022
Cited alongside, same era.
2023
Later among the works it cites.
Ryali, C., Hu, Y.T., Bolya, D., Wei, C., Fan, H., Huang, P.Y., Aggarwal, V., Chowdhury, A., Poursaeed, O., Hoffman, J., et al.: Hiera: A hierarchical vision transformer without the bells-and-whistles. In: ICML. pp. 29441–29454. PMLR (2023)
2023
Later among the works it cites.
Wang, X., Zhang, X., Cao, Y., Wang, W., Shen, C., Huang, T.: Seggpt: Towards segmenting everything in context. In: ICCV. pp. 1130–1140 (2023)
2023
Later among the works it cites.
Wang, Y., Wang, R., Fan, X., Wang, T., He, X.: Pixels, regions, and objects: Multiple enhancement for salient object detection. In: CVPR. pp. 10031–10040 (2023)
2023
Later among the works it cites.
Xiong, X., Wang, C., Li, W., Li, G.: Mammo-sam: Adapting foundation segment anything model for automatic breast mass segmentation in whole mammograms. In: MLMI. pp. 176–185. Springer (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Zhou, T., Zhou, Y., He, K., Gong, C., Yang, J., Fu, H., Shen, D.: Cross-level feature aggregation network for polyp segmentation. Pattern Recognition 140
2023
Later among the works it cites.
Chen, J., Mei, J., Li, X., Lu, Y., Yu, Q., Wei, Q., Luo, X., Xie, Y., Adeli, E., Wang, Y., et al.: Transunet: Rethinking the u-net architecture design for medical image segmentation through the lens of transformers. Medical Image Analysis p. 103280 (2024)
2024
Closest in time.
Huang, D., Xiong, X., Ma, J., Li, J., Jie, Z., Ma, L., Li, G.: Alignsam: Aligning segment anything model to open context via reinforcement learning. In: CVPR. pp. 3205–3215 (2024)
2024
Closest in time.
2024
Closest in time.
Li, X., Yuan, H., Li, W., Ding, H., Wu, S., Zhang, W., Li, Y., Chen, K., Loy, C.C.: Omg-seg: Is one model good enough for all segmentation? In: CVPR. pp. 27948–27959 (2024)
2024
Closest in time.
Liu, Y., Zhu, M., Li, H., Chen, H., Wang, X., Shen, C.: Matcher: Segment anything with one shot using all-purpose feature matching. In: ICLR (2024)
2024
Closest in time.
2024
Closest in time.
Zhang, R., Jiang, Z., Guo, Z., Yan, S., Pan, J., Dong, H., Qiao, Y., Gao, P., Li, H.: Personalize segment anything model with one shot. In: ICLR (2024)
2024
Closest in time.