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Temporal Action Detection (TAD) focuses on detecting pre-defined actions, while Moment Retrieval (MR) aims to identify the events described by open-ended natural language within untrimmed videos.
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Bodla, N., Singh, B., Chellappa, R., Davis, L.S.: Soft-nms–improving object detection with one line of code. In: Proceedings of the IEEE international conference on computer vision. pp. 5561–5569 (2017)
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Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6299–6308 (2017)
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Gao, J., Sun, C., Yang, Z., Nevatia, R.: Tall: Temporal activity localization via language query. In: Proceedings of the IEEE international conference on computer vision. pp. 5267–5275 (2017)
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Krishna, R., Hata, K., Ren, F., Fei-Fei, L., Carlos Niebles, J.: Dense-captioning events in videos. In: Proceedings of the IEEE international conference on computer vision. pp. 706–715 (2017)
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Lin, T., Zhao, X., Shou, Z.: Single shot temporal action detection. In: Proceedings of the 25th ACM international conference on Multimedia. pp. 988–996 (2017)
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Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 2980–2988 (2017)
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Zhao, Y., Xiong, Y., Wang, L., Wu, Z., Tang, X., Lin, D.: Temporal action detection with structured segment networks. In: Proceedings of the IEEE international conference on computer vision. pp. 2914–2923 (2017)
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Feichtenhofer, C., Fan, H., Malik, J., He, K.: Slowfast networks for video recognition. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 6202–6211 (2019)
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Piergiovanni, A., Ryoo, M.: Temporal gaussian mixture layer for videos. In: International Conference on Machine learning. pp. 5152–5161. PMLR (2019)
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Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 9627–9636 (2019)
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Yuan, Y., Ma, L., Wang, J., Liu, W., Zhu, W.: Semantic conditioned dynamic modulation for temporal sentence grounding in videos. Advances in Neural Information Processing Systems 32
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Yuan, Y., Mei, T., Zhu, W.: To find where you talk: Temporal sentence localization in video with attention based location regression. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 33, pp. 9159–9166 (2019)
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Zeng, R., Huang, W., Tan, M., Rong, Y., Zhao, P., Huang, J., Gan, C.: Graph convolutional networks for temporal action localization. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 7094–7103 (2019)
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Zhang, D., Dai, X., Wang, X., Wang, Y.F., Davis, L.S.: Man: Moment alignment network for natural language moment retrieval via iterative graph adjustment. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1247–1257 (2019)
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Chen, L., Lu, C., Tang, S., Xiao, J., Zhang, D., Tan, C., Li, X.: Rethinking the bottom-up framework for query-based video localization. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 10551–10558 (2020)
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Feichtenhofer, C.: X3d: Expanding architectures for efficient video recognition. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 203–213 (2020)
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Mavroudi, E., Haro, B.B., Vidal, R.: Representation learning on visual-symbolic graphs for video understanding. In: European Conference on Computer Vision. pp. 71–90. Springer (2020)
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Mun, J., Cho, M., Han, B.: Local-global video-text interactions for temporal grounding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10810–10819 (2020)
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Tan, M., Pang, R., Le, Q.V.: Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10781–10790 (2020)
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Zeng, R., Xu, H., Huang, W., Chen, P., Tan, M., Gan, C.: Dense regression network for video grounding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10287–10296 (2020)
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2020
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Zhang, S., Peng, H., Fu, J., Luo, J.: Learning 2d temporal adjacent networks for moment localization with natural language. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 12870–12877 (2020)
2020
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Alwassel, H., Giancola, S., Ghanem, B.: Tsp: Temporally-sensitive pretraining of video encoders for localization tasks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3173–3183 (2021)
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Grauman, K., Westbury, A., Byrne, E., Chavis, Z., Furnari, A., Girdhar, R., Hamburger, J., Jiang, H., Liu, M., Liu, X., et al.: Ego4d: Around the world in 3,000 hours of egocentric video. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18995–19012 (2022)
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Ju, C., Han, T., Zheng, K., Zhang, Y., Xie, W.: Prompting visual-language models for efficient video understanding. In: European Conference on Computer Vision. pp. 105–124. Springer (2022)
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Lin, K.Q., Wang, J., Soldan, M., Wray, M., Yan, R., XU, E.Z., Gao, D., Tu, R.C., Zhao, W., Kong, W., et al.: Egocentric video-language pretraining. Advances in Neural Information Processing Systems 35
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Liu, X., Wang, Q., Hu, Y., Tang, X., Zhang, S., Bai, S., Bai, X.: End-to-end temporal action detection with transformer. IEEE Transactions on Image Processing 31
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2021
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Dai, R., Das, S., Minciullo, L., Garattoni, L., Francesca, G., Bremond, F.: Pdan: Pyramid dilated attention network for action detection. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 2970–2979 (2021)
2021
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Jia, C., Yang, Y., Xia, Y., Chen, Y.T., Parekh, Z., Pham, H., Le, Q., Sung, Y.H., Li, Z., Duerig, T.: Scaling up visual and vision-language representation learning with noisy text supervision. In: International conference on machine learning. pp. 4904–4916. PMLR (2021)
2021
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Kahatapitiya, K., Ryoo, M.S.: Coarse-fine networks for temporal activity detection in videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8385–8394 (2021)
2021
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Lei, J., Berg, T.L., Bansal, M.: Detecting moments and highlights in videos via natural language queries. Advances in Neural Information Processing Systems 34
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Lin, C., Xu, C., Luo, D., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Fu, Y.: Learning salient boundary feature for anchor-free temporal action localization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3320–3329 (2021)
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Qing, Z., Su, H., Gan, W., Wang, D., Wu, W., Wang, X., Qiao, Y., Yan, J., Gao, C., Sang, N.: Temporal context aggregation network for temporal action proposal refinement. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 485–494 (2021)
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2021
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Liu, Y., Li, S., Wu, Y., Chen, C.W., Shan, Y., Qie, X.: Umt: Unified multi-modal transformers for joint video moment retrieval and highlight detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3042–3051 (2022)
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Nag, S., Zhu, X., Song, Y.Z., Xiang, T.: Zero-shot temporal action detection via vision-language prompting. In: European Conference on Computer Vision. pp. 681–697. Springer (2022)
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Tong, Z., Song, Y., Wang, J., Wang, L.: Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training. Advances in neural information processing systems 35
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Zhang, C.L., Wu, J., Li, Y.: Actionformer: Localizing moments of actions with transformers. In: European Conference on Computer Vision. pp. 492–510. Springer (2022)
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Zhong, Y., Yang, J., Zhang, P., Li, C., Codella, N., Li, L.H., Zhou, L., Dai, X., Yuan, L., Li, Y., et al.: Regionclip: Region-based language-image pretraining. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16793–16803 (2022)
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Kahatapitiya, K., Ren, Z., Li, H., Wu, Z., Ryoo, M.S., Hua, G.: Weakly-guided self-supervised pretraining for temporal activity detection. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 37, pp. 1078–1086 (2023)
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Moon, W., Hyun, S., Park, S., Park, D., Heo, J.P.: Query-dependent video representation for moment retrieval and highlight detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 23023–23033 (2023)
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Sardari, F., Mustafa, A., Jackson, P.J., Hilton, A.: Pat: Position-aware transformer for dense multi-label action detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2988–2997 (2023)
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Li, Z., Zhong, Y., Song, R., Li, T., Ma, L., Zhang, W.: Detal: Open-vocabulary temporal action localization with decoupled networks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)
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