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The recently introduced Segment Anything Model (SAM) combines a clever architecture and large quantities of training data to obtain remarkable image segmentation capabilities.
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: CVPR. pp. 1597–1604. IEEE, Miami, FL, USA (2009)
2009
Earlier work this paper cites.
Sharma, N., Aggarwal, L.M.: Automated medical image segmentation techniques. Journal of medical physics/Association of Medical Physicists of India 35
2010
Earlier work this paper cites.
Margolin, R., Zelnik-Manor, L., Tal, A.: How to evaluate foreground maps? In: CVPR. pp. 248–255. IEEE (2014)
2014
Earlier work this paper cites.
Silva, J., Histace, A., Romain, O., et al.: Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer. IJCARS (2014)
2014
Earlier work this paper cites.
Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., et al.: WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Computerized Medical Imaging and Graphics 43
2015
Earlier work this paper cites.
Borji, A., Cheng, M.M., Jiang, H., Li, J.: Salient object detection: A benchmark. IEEE TIP 24
2015
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI (2015)
2015
Earlier work this paper cites.
Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automated polyp detection in colonoscopy videos using shape and context information. TMI 35
2015
Earlier work this paper cites.
Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. TPAMI 39
2017
Earlier work this paper cites.
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. IEEE, Venice, Italy (2017)
2017
Earlier work this paper cites.
Sirinukunwattana, K., Pluim, J.P., Chen, H., Qi, X., Heng, P.A., Guo, Y.B., Wang, L.Y., Matuszewski, B.J., Bruni, E., Sanchez, U., et al.: Gland segmentation in colon histology images: The glas challenge contest. Medical image analysis 35
2017
Earlier work this paper cites.
Fan, D.P., Gong, C., Cao, Y., Ren, B., Cheng, M.M., Borji, A.: Enhanced-alignment measure for binary foreground map evaluation. In: IJCAI. pp. 698–704. IJCAI, Stockholm, Sweden (2018)
2018
Earlier work this paper cites.
Norman, B., Pedoia, V., Majumdar, S.: Use of 2d u-net convolutional neural networks for automated cartilage and meniscus segmentation of knee mr imaging data to determine relaxometry and morphometry. Radiology 288
2018
Earlier work this paper cites.
Xiao, X., Lian, S., Luo, Z., Li, S.: Weighted res-unet for high-quality retina vessel segmentation. In: ITME. IEEE (2018)
2018
Earlier work this paper cites.
Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: A nested u-net architecture for medical image segmentation. In: DLMIA. Springer (2018)
2018
Earlier work this paper cites.
Chao, P., Kao, C.Y., Ruan, Y.S., Huang, C.H., Lin, Y.L.: Hardnet: A low memory traffic network. In: ICCV (2019)
2019
Earlier work this paper cites.
Fang, Y., Chen, C., et al.: Selective feature aggregation network with area-boundary constraints for polyp segmentation. In: MICCAI (2019)
2019
Earlier work this paper cites.
Kumar, N., Verma, R., Anand, D., Zhou, Y., Onder, O.F., et al.: A multi-organ nucleus segmentation challenge. TMI (2019)
2019
Earlier work this paper cites.
Lu, X., Wang, W., Ma, C., Shen, J., Shao, L., Porikli, F.: See more, know more: Unsupervised video object segmentation with co-attention siamese networks. In: CVPR. pp. 3623–3632. IEEE (2019)
2019
Earlier work this paper cites.
Wallace, E., Feng, S., Kandpal, N., Gardner, M., Singh, S.: Universal adversarial triggers for attacking and analyzing NLP. EMNLP (2019)
2019
Earlier work this paper cites.
Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation 39
2019
Cited alongside, same era.
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. Springer (2020)
2020
Cited alongside, same era.
Fan, D.P., Ji, G.P., Zhou, T., Chen, G., Fu, H., Shen, J., Shao, L.: Pranet: Parallel reverse attention network for polyp segmentation. pp. 263–273. Springer (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Gu, Y., Wang, L., Wang, Z., Liu, Y., Cheng, M.M., Lu, S.P.: Pyramid constrained self-attention network for fast video salient object detection. In: AAAI. vol. 34, pp. 10869–10876. AAAI Press (2020)
Liu, R., Wu, Z., Yu, S., Lin, S.: The emergence of objectness: Learning zero-shot segmentation from videos. In: NeurIPS. Curran Associates, Inc., [Online] (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Misawa, M., Kudo, S.e., Mori, Y., Hotta, K., Ohtsuka, K., Matsuda, T., Saito, S., Kudo, T., Baba, T., Ishida, F., et al.: Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database (with video). Gastrointestinal endoscopy 93
2021
Later among the works it cites.
Patel, K., Bur, A.M., Wang, G.: Enhanced u-net: A feature enhancement network for polyp segmentation. In: CRV. IEEE (2021)
2021
Later among the works it cites.
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2020
Cited alongside, same era.
Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., Lange, T.d., Johansen, D., Johansen, H.D.: Kvasir-seg: A segmented polyp dataset. In: MMM. Springer (2020)
2020
Cited alongside, same era.
Puyal, J.G.B., Bhatia, K.K., Brandao, P., Ahmad, O.F., Toth, D., Kader, R., Lovat, L., Mountney, P., Stoyanov, D.: Endoscopic polyp segmentation using a hybrid 2d/3d cnn. pp. 295–305. Springer (2020)
2020
Cited alongside, same era.
Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. EMNLP (2020)
2020
Cited alongside, same era.
Wang, H., Zhu, Y., Green, B., Adam, H., Yuille, A., Chen, L.C.: Axial-deeplab: Stand-alone axial-attention for panoptic segmentation. In: ECCV. Springer (2020)
2020
Cited alongside, same era.
Zhang, R., Li, G., Li, Z., Cui, S., Qian, D., Yu, Y.: Adaptive context selection for polyp segmentation. In: MICCAI. Springer (2020)
2020
Cited alongside, same era.
Zhou, T., Li, J., Wang, S., Tao, R., Shen, J.: Matnet: Motion-attentive transition network for zero-shot video object segmentation. IEEE TIP 29
2020
Cited alongside, same era.
Cheng, M.M., Fan, D.P.: Structure-measure: A new way to evaluate foreground maps. IJCV 129
2021
Cited alongside, same era.
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., Sutskever, I.: Zero-shot text-to-image generation. In: International Conference on Machine Learning. pp. 8821–8831. PMLR (2021)
2021
Later among the works it cites.
Strudel, R., Garcia, R., Laptev, I., Schmid, C.: Segmenter: Transformer for semantic segmentation. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 7262–7272 (2021)
2021
Later among the works it cites.
Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., Patel, V.M.: Medical transformer: Gated axial-attention for medical image segmentation. arXiv (2021)
2021
Later among the works it cites.
Wang, H., Cao, P., Wang, J., Zaiane, O.R.: Uctransnet: Rethinking the skip connections in u-net from a channel-wise perspective with transformer. arXiv (2021)
2021
Later among the works it cites.
Wei, J., Hu, Y., Zhang, R., Li, Z., Zhou, S.K., Cui, S.: Shallow attention network for polyp segmentation. In: MICCAI. Springer (2021)
2021
Later among the works it cites.
Yin, Z., Liang, K., Ma, Z., Guo, J.: Duplex contextual relation network for polyp segmentation. arXiv (2021)
2021
Later among the works it cites.
Zhang, M., Liu, J., Wang, Y., Piao, Y., Yao, S., Ji, W., Li, J., Lu, H., Luo, Z.: Dynamic context-sensitive filtering network for video salient object detection. In: ICCV. pp. 1553–1563. IEEE (2021)
2021
Later among the works it cites.
Ji, G.P., Xiao, G., Chou, Y.C., Fan, D.P., Zhao, K., Chen, G., Van Gool, L.: Video polyp segmentation: A deep learning perspective. Machine Intelligence Research 19
2022
Later among the works it cites.
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S.K.S., Ayan, B.K., Mahdavi, S.S., Lopes, R.G., Salimans, T., Ho, J., Fleet, D.J., Norouzi, M.: Photorealistic text-to-image diffusion models with deep language understanding (2022)
2022
Later among the works it cites.
Shaharabany, T., Wolf, L.: End-to-end segmentation of medical images via patch-wise polygons prediction. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part V. pp. 308–318. Springer (2022)
2022
Later among the works it cites.
Tewel, Y., Shalev, Y., Nadler, R., Schwartz, I., Wolf, L.: Zero-shot video captioning with evolving pseudo-tokens (2022)
2022
Later among the works it cites.
Tewel, Y., Shalev, Y., Schwartz, I., Wolf, L.: Zero-shot image-to-text generation for visual-semantic arithmetic. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2022)
2022
Later among the works it cites.
2023
Closest in time.
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., Lample, G.: Llama: Open and efficient foundation language models (2023)
2023
Closest in time.