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Detecting glass regions is a challenging task due to the inherent ambiguity in their transparency and reflective characteristics.
T. Wang, X. He, and N. Barnes, “Glass object segmentation by label transfer on joint depth and appearance manifolds,” in 2013 IEEE International Conference on Image Processing . IEEE, 2013, pp. 2944–2948
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Y. Xu, H. Nagahara, A. Shimada, and R.-i. Taniguchi, “Transcut: Transparent object segmentation from a light-field image,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 3442–3450
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M. Meng, M. Lan, J. Yu, and J. Wu, “Coupled knowledge transfer for visual data recognition,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 31, no. 5, pp. 1776–1789, 2020
2020
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H. Mei, X. Yang, Y. Wang, Y. Liu, S. He, Q. Zhang, X. Wei, and R. W. Lau, “Don’t hit me! glass detection in real-world scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 3687–3696
2020
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A. Kalra, V. Taamazyan, S. K. Rao, K. Venkataraman, R. Raskar, and A. Kadambi, “Deep polarization cues for transparent object segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 8602–8611
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J. Lin, Z. He, and R. W. Lau, “Rich context aggregation with reflection prior for glass surface detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 13 415–13 424
2021
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H. He, X. Li, G. Cheng, J. Shi, Y. Tong, G. Meng, V. Prinet, and L. Weng, “Enhanced boundary learning for glass-like object segmentation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 15 859–15 868
2021
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A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
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H. Mei, B. Dong, W. Dong, P. Peers, X. Yang, Q. Zhang, and X. Wei, “Depth-aware mirror segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 3044–3053
2021
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A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in International Conference on Machine Learning . PMLR, 2021, pp. 8821–8831
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S. Zheng, J. Lu, H. Zhao, X. Zhu, Z. Luo, Y. Wang, Y. Fu, J. Feng, T. Xiang, P. H. Torr et al. , “Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 6881–6890
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R. Strudel, R. Garcia, I. Laptev, and C. Schmid, “Segmenter: Transformer for semantic segmentation,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 7262–7272
2021
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Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 10 012–10 022
2021
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E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, “Segformer: Simple and efficient design for semantic segmentation with transformers,” Advances in Neural Information Processing Systems , vol. 34, pp. 12 077–12 090, 2021
2021
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A. Siris, J. Jiao, G. K. Tam, X. Xie, and R. W. Lau, “Scene context-aware salient object detection,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 4156–4166
2021
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L. Yuan, Y. Chen, T. Wang, W. Yu, Y. Shi, Z.-H. Jiang, F. E. Tay, J. Feng, and S. Yan, “Tokens-to-token vit: Training vision transformers from scratch on imagenet,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 558–567
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X. Chu, Z. Tian, Y. Wang, B. Zhang, H. Ren, X. Wei, H. Xia, and C. Shen, “Twins: Revisiting the design of spatial attention in vision transformers,” Advances in Neural Information Processing Systems , vol. 34, pp. 9355–9366, 2021
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B. Cheng, A. G. Schwing, and A. Kirillov, “Per-pixel classification is not all you need for semantic segmentation,” 2021
2021
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J. Lin, Y.-H. Yeung, and R. Lau, “Exploiting semantic relations for glass surface detection,” Advances in Neural Information Processing Systems , vol. 35, pp. 22 490–22 504, 2022
2022
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H. Mei, B. Dong, W. Dong, J. Yang, S.-H. Baek, F. Heide, P. Peers, X. Wei, and X. Yang, “Glass segmentation using intensity and spectral polarization cues,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 12 622–12 631
2022
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R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
2022
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Y. Li, H. Mao, R. Girshick, and K. He, “Exploring plain vision transformer backbones for object detection,” in European Conference on Computer Vision . Springer, 2022, pp. 280–296
2022
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2023
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L. Zhang, A. Rao, and M. Agrawala, “Adding conditional control to text-to-image diffusion models,” 2023
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D. Huo, J. Wang, Y. Qian, and Y.-H. Yang, “Glass segmentation with rgb-thermal image pairs,” IEEE Transactions on Image Processing , vol. 32, pp. 1911–1926, 2023
2023
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