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Referring Image Segmentation~(RIS) leveraging transformers has achieved great success on the interpretation of complex visual-language tasks.
Kazemzadeh, S., Ordonez, V., Matten, M., Berg, T.: ReferItGame: Referring to objects in photographs of natural scenes. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014)
2014
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: Common Objects in Context. ECCV (2014)
2014
Earlier work this paper cites.
Mao, J., Huang, J., Toshev, A., Camburu, O.M., Yuille, A., Murphy, K.: Generation and comprehension of unambiguous object descriptions. CVPR (2015)
2015
Earlier work this paper cites.
Hu, R., Rohrbach, M., Darrell, T.: Segmentation from natural language expressions. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14. pp. 108–124. Springer (2016)
2016
Earlier work this paper cites.
Nagaraja, V.K., Morariu, V.I., Davis, L.S.: Modeling context between objects for referring expression understanding. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV (2016)
2016
Earlier work this paper cites.
Liu, C., Lin, Z., Shen, X., Yang, J., Lu, X., Yuille, A.: Recurrent multimodal interaction for referring image segmentation. In: Proceedings of the IEEE international conference on computer vision. pp. 1271–1280 (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Li, R., Li, K., Kuo, Y.C., Shu, M., Qi, X., Shen, X., Jia, J.: Referring image segmentation via recurrent refinement networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5745–5753 (2018)
2018
Earlier work this paper cites.
Margffoy-Tuay, E., Pérez, J.C., Botero, E., Arbeláez, P.: Dynamic multimodal instance segmentation guided by natural language queries. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 630–645 (2018)
2018
Earlier work this paper cites.
Perez, E., Strub, F., de Vries, H., Dumoulin, V., Courville, A.C.: Film: Visual reasoning with a general conditioning layer. In: AAAI (2018)
2018
Earlier work this paper cites.
Shi, H., Li, H., Meng, F., Wu, Q.: Key-word-aware network for referring expression image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 38–54 (2018)
2018
Earlier work this paper cites.
Yu, L., Lin, Z., Shen, X., Yang, J., Lu, X., Bansal, M., Berg, T.L.: Mattnet: Modular attention network for referring expression comprehension. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)
2018
Earlier work this paper cites.
Chen, D.J., Jia, S., Lo, Y.C., Chen, H.T., Liu, T.L.: See-through-text grouping for referring image segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7454–7463 (2019)
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Ye, L., Rochan, M., Liu, Z., Wang, Y.: Cross-modal self-attention network for referring image segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10502–10511 (2019)
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
Gu, A., Dao, T., Ermon, S., Rudra, A., Ré, C.: Hippo: Recurrent memory with optimal polynomial projections. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Hu, Z., Feng, G., Sun, J., Zhang, L., Lu, H.: Bi-directional relationship inferring network for referring image segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4424–4433 (2020)
2020
Earlier work this paper cites.
Huang, S., Hui, T., Liu, S., Li, G., Wei, Y., Han, J., Liu, L., Li, B.: Referring image segmentation via cross-modal progressive comprehension. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10488–10497 (2020)
2020
Earlier work this paper cites.
Hui, T., Liu, S., Huang, S., Li, G., Yu, S., Zhang, F., Han, J.: Linguistic structure guided context modeling for referring image segmentation. In: European Conference on Computer Vision. pp. 59–75. Springer (2020)
2020
Earlier work this paper cites.
Luo, G., Zhou, Y., Ji, R., Sun, X., Su, J., Lin, C.W., Tian, Q.: Cascade grouped attention network for referring expression segmentation. Proceedings of the 28th ACM International Conference on Multimedia (2020)
2020
Earlier work this paper cites.
Ding, H., Liu, C., Wang, S., Jiang, X.: Vision-language transformer and query generation for referring segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 16321–16330 (2021)
2021
Cited alongside, same era.
Feng, G., Hu, Z., Zhang, L., Lu, H.: Encoder fusion network with co-attention embedding for referring image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15506–15515 (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Gu, A., Johnson, I., Goel, K., Saab, K., Dao, T., Rudra, A., Ré, C.: Combining recurrent, convolutional, and continuous-time models with linear state space layers. Advances in neural information processing systems 34
2021
Cited alongside, same era.
Islam, M.M., Hasan, M., Athrey, K.S., Braskich, T., Bertasius, G.: Efficient movie scene detection using state-space transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18749–18758 (2023)
2023
Later among the works it cites.
Liu, C., Ding, H., Jiang, X.: Gres: Generalized referring expression segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 23592–23601 (2023)
2023
Later among the works it cites.
Liu, J., Ding, H., Cai, Z., Zhang, Y., Satzoda, R.K., Mahadevan, V., Manmatha, R.: Polyformer: Referring image segmentation as sequential polygon generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18653–18663 (2023)
2023
Later among the works it cites.
Ma, C., Yang, Y., Ju, C., Zhang, F., Zhang, Y., Wang, Y.: Attrseg: Open-vocabulary semantic segmentation via attribute decomposition-aggregation. NeurIPS (2023)
2023
Later among the works it cites.
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Jing, Y., Kong, T., Wang, W., Wang, L., Li, L., Tan, T.: Locate then segment: A strong pipeline for referring image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9858–9867 (2021)
2021
Cited alongside, same era.
Kamath, A., Singh, M., LeCun, Y., Synnaeve, G., Misra, I., Carion, N.: Mdetr-modulated detection for end-to-end multi-modal understanding. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1780–1790 (2021)
2021
Cited alongside, same era.
Liu, S., Hui, T., Huang, S., Wei, Y., Li, B., Li, G.: Cross-modal progressive comprehension for referring segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44
2021
Cited alongside, same era.
Yang, S., Xia, M., Li, G., Zhou, H.Y., Yu, Y.: Bottom-up shift and reasoning for referring image segmentation. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 11261–11270 (2021)
2021
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Gu, A., Goel, K., Gupta, A., Ré, C.: On the parameterization and initialization of diagonal state space models. Advances in Neural Information Processing Systems 35
2022
Cited alongside, same era.
Gupta, A., Gu, A., Berant, J.: Diagonal state spaces are as effective as structured state spaces. Advances in Neural Information Processing Systems 35
2022
Cited alongside, same era.
Qu, M., Wu, Y., Wei, Y., Liu, W., Liang, X., Zhao, Y.: Learning to segment every referring object point by point. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023)
2023
Later among the works it cites.
Tang, J., Zheng, G., Shi, C., Yang, S.: Contrastive grouping with transformer for referring image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 23570–23580 (2023)
2023
Later among the works it cites.
Tang, J., Zheng, G., Shi, C., Yang, S.: Contrastive grouping with transformer for referring image segmentation. In: CVPR (2023)
2023
Later among the works it cites.
Wang, J., Zhu, W., Wang, P., Yu, X., Liu, L., Omar, M., Hamid, R.: Selective structured state-spaces for long-form video understanding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6387–6397 (2023)
2023
Later among the works it cites.
Xu, Z., Chen, Z., Zhang, Y., Song, Y., Wan, X., Li, G.: Bridging vision and language encoders: Parameter-efficient tuning for referring image segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 17503–17512 (2023)
2023
Later among the works it cites.
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Yang, Y., Ma, C., Ju, C., Zhang, Y., Wang, Y.: Multi-modal prototypes for open-set semantic segmentation. IJCV (2024)
2024
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Zhang, T., Li, X., Yuan, H., Ji, S., Yan, S.: Point could mamba: Point cloud learning via state space model. arXiv preprint arXiv: 2403.00762 (2024)
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