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Referring Image Segmentation (RIS), aims to segment the object referred by a given sentence in an image by understanding both visual and linguistic information.
R. Hu, M. Rohrbach, and T. Darrell, “Segmentation from natural language expressions,” in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14 , 2016, pp. 108–124
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber, “Lstm: A search space odyssey,” IEEE transactions on neural networks and learning systems , vol. 28, no. 10, pp. 2222–2232, 2016
2016
Earlier work this paper cites.
L. Yu, P. Poirson, S. Yang, A. C. Berg, and T. L. Berg, “Modeling context in referring expressions,” in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14 , 2016, pp. 69–85
2016
Earlier work this paper cites.
J. Mao, J. Huang, A. Toshev, O. Camburu, A. L. Yuille, and K. Murphy, “Generation and comprehension of unambiguous object descriptions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 11–20
2016
Earlier work this paper cites.
C. Liu, Z. Lin, X. Shen, J. Yang, X. Lu, and A. Yuille, “Recurrent multimodal interaction for referring image segmentation,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 1271–1280
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
E. Margffoy-Tuay, J. C. Pérez, E. Botero, and P. Arbeláez, “Dynamic multimodal instance segmentation guided by natural language queries,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 630–645
2018
Earlier work this paper cites.
R. Li, K. Li, Y.-C. Kuo, M. Shu, X. Qi, X. Shen, and J. Jia, “Referring image segmentation via recurrent refinement networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 5745–5753
2018
Earlier work this paper cites.
L. Yu, Z. Lin, X. Shen, J. Yang, X. Lu, M. Bansal, and T. L. Berg, “Mattnet: Modular attention network for referring expression comprehension,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 1307–1315
2018
Earlier work this paper cites.
H. Shi, H. Li, F. Meng, and Q. Wu, “Key-word-aware network for referring expression image segmentation,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 38–54
2018
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2019
Earlier work this paper cites.
L. Ye, M. Rochan, Z. Liu, and Y. Wang, “Cross-modal self-attention network for referring image segmentation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 10 502–10 511
2019
Earlier work this paper cites.
J. D. M.-W. C. Kenton and L. K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of naacL-HLT , vol. 1, 2019, p. 2
2019
Earlier work this paper cites.
R. Li, Y. Wang, F. Liang, H. Qin, J. Yan, and R. Fan, “Fully quantized network for object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 2810–2819
2019
Earlier work this paper cites.
Y. Choukroun, E. Kravchik, F. Yang, and P. Kisilev, “Low-bit quantization of neural networks for efficient inference,” in 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) . IEEE, 2019, pp. 3009–3018
2019
Earlier work this paper cites.
A. M. Hafiz and G. M. Bhat, “A survey on instance segmentation: state of the art,” International journal of multimedia information retrieval , vol. 9, no. 3, pp. 171–189, 2020
2020
Earlier work this paper cites.
S. Hao, Y. Zhou, and Y. Guo, “A brief survey on semantic segmentation with deep learning,” Neurocomputing , vol. 406, pp. 302–321, 2020
2020
Earlier work this paper cites.
Z. Hu, G. Feng, J. Sun, L. Zhang, and H. Lu, “Bi-directional relationship inferring network for referring image segmentation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 4424–4433
2020
Earlier work this paper cites.
S. Huang, T. Hui, S. Liu, G. Li, Y. Wei, J. Han, L. Liu, and B. Li, “Referring image segmentation via cross-modal progressive comprehension,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 10 488–10 497
2020
Cited alongside, same era.
G. Luo, Y. Zhou, R. Ji, X. Sun, J. Su, C.-W. Lin, and Q. Tian, “Cascade grouped attention network for referring expression segmentation,” in Proceedings of the 28th ACM International Conference on Multimedia , 2020, pp. 1274–1282
2020
Cited alongside, same era.
T. Hui, S. Liu, S. Huang, G. Li, S. Yu, F. Zhang, and J. Han, “Linguistic structure guided context modeling for referring image segmentation,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part X 16 . Springer, 2020, pp. 59–75
2020
Cited alongside, same era.
M. Nagel, R. A. Amjad, M. Van Baalen, C. Louizos, and T. Blankevoort, “Up or down? adaptive rounding for post-training quantization,” in International Conference on Machine Learning . PMLR, 2020, pp. 7197–7206
Z. Wang, Y. Lu, Q. Li, X. Tao, Y. Guo, M. Gong, and T. Liu, “Cris: Clip-driven referring image segmentation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 11 686–11 695
2022
Later among the works it cites.
C. Zhu, Y. Zhou, Y. Shen, G. Luo, X. Pan, M. Lin, C. Chen, L. Cao, X. Sun, and R. Ji, “Seqtr: A simple yet universal network for visual grounding,” in European Conference on Computer Vision , 2022, pp. 598–615
2022
Later among the works it cites.
H. Qin, Y. Ding, M. Zhang, Y. Qinghua, A. Liu, Q. Dang, Z. Liu, and X. Liu, “Bibert: Accurate fully binarized bert,” in International Conference on Learning Representations , 2022
2022
Later among the works it cites.
A. Gholami, S. Kim, Z. Dong, Z. Yao, M. W. Mahoney, and K. Keutzer, “A survey of quantization methods for efficient neural network inference,” in Low-Power Computer Vision , 2022, pp. 291–326
2022
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2020
Cited alongside, same era.
2020
Cited alongside, same era.
H. Ding, C. Liu, S. Wang, and X. Jiang, “Vision-language transformer and query generation for referring segmentation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 16 321–16 330
2021
Cited alongside, same era.
Y. Nahshan, B. Chmiel, C. Baskin, E. Zheltonozhskii, R. Banner, A. M. Bronstein, and A. Mendelson, “Loss aware post-training quantization,” Machine Learning , vol. 110, no. 11, pp. 3245–3262, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Z. Liu, Y. Wang, K. Han, W. Zhang, S. Ma, and W. Gao, “Post-training quantization for vision transformer,” Advances in Neural Information Processing Systems , vol. 34, pp. 28 092–28 103, 2021
2021
Cited alongside, same era.
S. Kim, A. Gholami, Z. Yao, M. W. Mahoney, and K. Keutzer, “I-bert: Integer-only bert quantization,” in International conference on machine learning , 2021, pp. 5506–5518
2021
Cited alongside, same era.
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
Cited alongside, same era.
Z. Yang, J. Wang, Y. Tang, K. Chen, H. Zhao, and P. H. Torr, “Semantics-aware dynamic localization and refinement for referring image segmentation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 3, 2023, pp. 3222–3230
2023
Later among the works it cites.
P.-L. Guhur, S. Chen, R. G. Pinel, M. Tapaswi, I. Laptev, and C. Schmid, “Instruction-driven history-aware policies for robotic manipulations,” in Conference on Robot Learning , 2023, pp. 175–187
2023
Later among the works it cites.
C. Lynch, A. Wahid, J. Tompson, T. Ding, J. Betker, R. Baruch, T. Armstrong, and P. Florence, “Interactive language: Talking to robots in real time,” IEEE Robotics and Automation Letters , 2023
2023
Later among the works it cites.
D. An, Y. Qi, Y. Li, Y. Huang, L. Wang, T. Tan, and J. Shao, “Bevbert: Multimodal map pre-training for language-guided navigation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 2737–2748
2023
Later among the works it cites.
S. Ouyang, H. Wang, S. Xie, Z. Niu, R. Tong, Y.-W. Chen, and L. Lin, “Slvit: Scale-wise language-guided vision transformer for referring image segmentation,” in Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence , 2023, pp. 1294–1302
2023
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Z. Li, J. Xiao, L. Yang, and Q. Gu, “Repq-vit: Scale reparameterization for post-training quantization of vision transformers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 17 227–17 236
2023
Later among the works it cites.
G. Xiao, J. Lin, M. Seznec, H. Wu, J. Demouth, and S. Han, “Smoothquant: Accurate and efficient post-training quantization for large language models,” in International Conference on Machine Learning , 2023, pp. 38 087–38 099
2023
Later among the works it cites.
E. Frantar, S. Ashkboos, T. Hoefler, and D. Alistarh, “Gptq: Accurate post-training quantization for generative pre-trained transformers,” in The Eleventh International Conference on Learning Representations , 2023
2023
Later among the works it cites.
J. Tang, G. Zheng, C. Shi, and S. Yang, “Contrastive grouping with transformer for referring image segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 23 570–23 580
2023
Later among the works it cites.
J. Liu, H. Ding, Z. Cai, Y. Zhang, R. K. Satzoda, V. Mahadevan, and R. Manmatha, “Polyformer: Referring image segmentation as sequential polygon generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 18 653–18 663
2023
Later among the works it cites.
Z. Xu, Z. Chen, Y. Zhang, Y. Song, X. Wan, and G. Li, “Bridging vision and language encoders: Parameter-efficient tuning for referring image segmentation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 17 503–17 512
2023
Later among the works it cites.
J. Liu, L. Niu, Z. Yuan, D. Yang, X. Wang, and W. Liu, “Pd-quant: Post-training quantization based on prediction difference metric,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 24 427–24 437
2023
Later among the works it cites.
L. Ji, Y. Du, Y. Dang, W. Gao, and H. Zhang, “A survey of methods for addressing the challenges of referring image segmentation,” Neurocomputing , vol. 583, p. 127599, 2024
2024
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