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Segment anything model (SAM) has shown its spectacular performance in segmenting universal objects, especially when elaborate prompts are provided.
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J. Zhu, K. G. G. Samuel, S. Z. Masood, and M. F. Tappen, “Learning to recognize shadows in monochromatic natural images,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit , 2010, pp. 223–230
2010
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C.-T. Chen, C.-Y. Su, and W.-C. Kao, “An enhanced segmentation on vision-based shadow removal for vehicle detection,” in The 2010 International Conference on Green Circuits and Systems , 2010, pp. 679–682
2010
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J. Zhu, K. G. Samuel, S. Z. Masood, and M. F. Tappen, “Learning to recognize shadows in monochromatic natural images,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit , 2010, pp. 223–230
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J.-F. Lalonde, A. A. Efros, and S. G. Narasimhan, “Detecting ground shadows in outdoor consumer photographs,” in Proc. Eur. Conf. Comput. Vis. , 2010, pp. 322–335
2010
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X. Huang, G. Hua, J. Tumblin, and L. Williams, “What characterizes a shadow boundary under the sun and sky?” in Proc. IEEE Int. Conf. Comput. Vis. , 2011, pp. 898–905
2011
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T. F. Y. Vicente, L. Hou, C.-P. Yu, M. Hoai, and D. Samaras, “Large-scale training of shadow detectors with noisily-annotated shadow examples,” in Proc. Eur. Conf. Comput. Vis. , 2016, pp. 816–832
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S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1492–1500
2017
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T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proc. IEEE Int. Conf. Comput. Vis. , 2017, pp. 2980–2988
2017
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V. Nguyen, T. F. Y. Vicente, M. Zhao, M. Hoai, and D. Samaras, “Shadow detection with conditional generative adversarial networks,” in Proc. IEEE Int. Conf. Comput. Vis. , 2017, pp. 4510–4518
2017
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J. Wang, X. Li, and J. Yang, “Stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit , 2018, pp. 1788–1797
2018
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X. Hu, L. Zhu, C.-W. Fu, J. Qin, and P.-A. Heng, “Direction-aware spatial context features for shadow detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit , 2018, pp. 7454–7462
2018
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L. Zhu, Z. Deng, X. Hu, C.-W. Fu, X. Xu, J. Qin, and P.-A. Heng, “Bidirectional feature pyramid network with recurrent attention residual modules for shadow detection,” in Proc. Eur. Conf. Comput. Vis. , 2018, pp. 121–136
2018
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S. Hosseinzadeh, M. Shakeri, and H. Zhang, “Fast shadow detection from a single image using a patched convolutional neural network,” in Proc. IEEE Int. Conf. Intell. Rob. Syst. , 2018, pp. 3124–3129
2018
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H. Le, T. F. Y. Vicente, V. Nguyen, M. Hoai, and D. Samaras, “A+D Net: Training a shadow detector with adversarial shadow attenuation,” in Proc. Eur. Conf. Comput. Vis. , 2018, pp. 662–678
2018
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Y. Wang, X. Zhao, Y. Li, X. Hu, and K. Huang, “Densely cascaded shadow detection network via deeply supervised parallel fusion,” in Proc. Int. Joint Conf. Artif. Intell. , 2018, pp. 1007–1013
2018
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Q. Zheng, X. Qiao, Y. Cao, and R. W. Lau, “Distraction-aware shadow detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit , 2019, pp. 5167–5176
2019
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M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in Proc. Int. Conf. Mach. Learn. , 2019, pp. 6105–6114
2019
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A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: An Imperative Style, High-Performance Deep Learning Library,” in Advances in Neural Information Processing Systems 32 , 2019, pp. 8024–8035
2019
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T. Wang, X. Hu, Q. Wang, P.-A. Heng, and C.-W. Fu, “Instance shadow detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit , 2020, pp. 1880–1889
2020
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L. Jie and H. Zhang, “When sam meets shadow detection,” arXiv preprint arXiv:2305.11513 , 2023
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——, “Rmlanet: Random multi-level attention network for shadow detection and removal,” IEEE Trans. Circuits Syst. Video Technol. , Early Access, 2023
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X. Hu, C.-W. Fu, L. Zhu, J. Qin, and P.-A. Heng, “Direction-aware spatial context features for shadow detection and removal,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 42, no. 11, pp. 2795–2808, 2020
2020
Cited alongside, same era.
Z. Chen, L. Zhu, L. Wan, S. Wang, W. Feng, and P.-A. Heng, “A multi-task mean teacher for semi-supervised shadow detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit , 2020, pp. 5611–5620
2020
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A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, S. Kamali, S. Popov, M. Malloci, A. Kolesnikov et al. , “The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale,” Int. J. Comput. Vis. , vol. 128, no. 7, pp. 1956–1981, 2020
2020
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2020
Cited alongside, same era.
X. Hu, T. Wang, C.-W. Fu, Y. Jiang, Q. Wang, and P.-A. Heng, “Revisiting shadow detection: A new benchmark dataset for complex world,” IEEE Trans. Image Process. , vol. 30, pp. 1925–1934, 2021
2021
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Z. Chen, L. Wan, L. Zhu, J. Shen, H. Fu, W. Liu, and J. Qin, “Triple-cooperative video shadow detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit , 2021, pp. 2715–2724
2021
Cited alongside, same era.
N. Inoue and T. Yamasaki, “Learning from synthetic shadows for shadow detection and removal,” IEEE Trans. Circuits Syst. Video Technol. , vol. 31, no. 11, pp. 4187–4197, 2021
2021
Cited alongside, same era.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” in Proc. Int. Conf. Learn. Representations. , 2021
2021
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2023
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J. Ma and B. Wang, “Segment anything in medical images,” arXiv:2304.12306 , 2023
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J. M. J. Valanarasu and V. M. Patel, “Fine-context shadow detection using shadow removal,” in Proc. IEEE Winter Conf. Appl. Comput. Vis , 2023, pp. 1705–1714
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J. Sun, K. Xu, Y. Pang, L. Zhang, H. Lu, G. Hancke, and R. Lau, “Adaptive illumination mapping for shadow detection in raw images,” in Proc. IEEE Int. Conf. Comput. Vis. , October 2023, pp. 12 709–12 718
2023
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H. Yang, T. Wang, X. Hu, and C.-W. Fu, “Silt: Shadow-aware iterative label tuning for learning to detect shadows from noisy labels,” in Proc. IEEE Int. Conf. Comput. Vis. , 2023, pp. 12 687–12 698
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R. Guo, Q. Dai, and D. Hoiem, “Single-image shadow detection and removal using paired regions,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit , 2011, pp. 2033–2040
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L. Shen, T. W. Chua, and K. Leman, “Shadow optimization from structured deep edge detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit , 2015, pp. 2067–2074
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