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In this paper, we propose a simple yet universal network termed SeqTR for visual grounding tasks, e.g., phrase localization, referring expression comprehension (REC) and segmentation (RES).
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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)
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Zhang, H., Niu, Y., Chang, S.F.: Grounding referring expressions in images by variational context. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4158–4166 (2018)
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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 (ICCV). pp. 7454–7463 (2019)
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Liao, Y., Liu, S., Li, G., Wang, F., Chen, Y., Qian, C., Li, B.: A real-time cross-modality correlation filtering method for referring expression comprehension. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 10880–10889 (2020)
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Luo, G., Zhou, Y., Ji, R., Sun, X., Su, J., Lin, C.W., Tian, Q.: Cascade grouped attention network for referring expression segmentation. In: Proceedings of the 28th ACM International Conference on Multimedia (MM). pp. 1274–1282 (2020)
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Luo, G., Zhou, Y., Sun, X., Cao, L., Wu, C., Deng, C., Ji, R.: Multi-task collaborative network for joint referring expression comprehension and segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 10034–10043 (2020)
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Yang, Z., Chen, T., Wang, L., Luo, J.: Improving one-stage visual grounding by recursive sub-query construction. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 387–404 (2020)
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Cited alongside, same era.
Hong, R., Liu, D., Mo, X., He, X., Zhang, H.: Learning to compose and reason with language tree structures for visual grounding. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2019)
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Liu, D., Zhang, H., Wu, F., Zha, Z.J.: Learning to assemble neural module tree networks for visual grounding. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 4673–4682 (2019)
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Liu, X., Wang, Z., Shao, J., Wang, X., Li, H.: Improving referring expression grounding with cross-modal attention-guided erasing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1950–1959 (2019)
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Lu, J., Batra, D., Parikh, D., Lee, S.: Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. Advances in Neural Information Processing Systems (NeurIPS) 32
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Sadhu, A., Chen, K., Nevatia, R.: Zero-shot grounding of objects from natural language queries. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 4694–4703 (2019)
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2020
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2021
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Deng, J., Yang, Z., Chen, T., Zhou, W., Li, H.: Transvg: End-to-end visual grounding with transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 1769–1779 (2021)
2021
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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 (ICCV). pp. 16321–16330 (2021)
2021
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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 (CVPR). pp. 15506–15515 (2021)
2021
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Ghiasi, G., Cui, Y., Srinivas, A., Qian, R., Lin, T.Y., Cubuk, E.D., Le, Q.V., Zoph, B.: Simple copy-paste is a strong data augmentation method for instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2918–2928 (2021)
2021
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Huang, B., Lian, D., Luo, W., Gao, S.: Look before you leap: Learning landmark features for one-stage visual grounding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 16888–16897 (2021)
2021
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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 (CVPR). pp. 9858–9867 (2021)
2021
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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 (ICCV). pp. 1780–1790 (2021)
2021
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Li, M., Sigal, L.: Referring transformer: A one-step approach to multi-task visual grounding. Advances in Neural Information Processing Systems (NeurIPS) 34
2021
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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 (TPAMI) (2021)
2021
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2021
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Sun, M., Xiao, J., Lim, E.G.: Iterative shrinking for referring expression grounding using deep reinforcement learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 14060–14069 (2021)
2021
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Yang, S., Xia, M., Li, G., Zhou, H.Y., Yu, Y.: Bottom-up shift and reasoning for referring image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 11266–11275 (2021)
2021
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Zhou, Y., Ji, R., Luo, G., Sun, X., Su, J., Ding, X., Lin, C.W., Tian, Q.: A real-time global inference network for one-stage referring expression comprehension. IEEE Transactions on Neural Networks and Learning Systems (TNNLS) (2021)
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Zhou, Y., Ren, T., Zhu, C., Sun, X., Liu, J., Ding, X., Xu, M., Ji, R.: Trar: Routing the attention spans in transformer for visual question answering. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 2074–2084 (2021)
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