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Temporal sentence grounding aims to localize moments relevant to a language description.
Kuhn, H.W.: The hungarian method for the assignment problem. Naval research logistics quarterly 2
1955
Earlier work this paper cites.
Rui, Y., Gupta, A., Acero, A.: Automatically extracting highlights for tv baseball programs. In: ACM MM. pp. 105–115 (2000)
2000
Earlier work this paper cites.
Regneri, M., Rohrbach, M., Wetzel, D., Thater, S., Schiele, B., Pinkal, M.: Grounding action descriptions in videos. Trans. Assoc. Comput. Linguistics 1
2013
Earlier work this paper cites.
Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: EMNLP. pp. 1532–1543 (2014)
2014
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
2015
Earlier work this paper cites.
Zhang, K., Chao, W.L., Sha, F., Grauman, K.: Video summarization with long short-term memory. In: ECCV. pp. 766–782. Springer (2016)
2016
Earlier work this paper cites.
Anne Hendricks, L., Wang, O., Shechtman, E., Sivic, J., Darrell, T., Russell, B.: Localizing moments in video with natural language. In: ICCV. pp. 5803–5812 (2017)
2017
Earlier work this paper cites.
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. PAMI 40
2017
Earlier work this paper cites.
Gao, J., Sun, C., Yang, Z., Nevatia, R.: Tall: Temporal activity localization via language query. In: CVPR. pp. 5267–5275 (2017)
2017
Earlier work this paper cites.
Gemmeke, J.F., Ellis, D.P., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: ICASSP. pp. 776–780. IEEE (2017)
2017
Earlier work this paper cites.
Sharghi, A., Laurel, J.S., Gong, B.: Query-focused video summarization: Dataset, evaluation, and a memory network based approach. In: CVPR. pp. 4788–4797 (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. In: Neurips. vol. 30 (2017)
2017
Earlier work this paper cites.
Zhao, Y., Xiong, Y., Wang, L., Wu, Z., Tang, X., Lin, D.: Temporal action detection with structured segment networks. In: ICCV. pp. 2914–2923 (2017)
2017
Earlier work this paper cites.
Chen, J., Chen, X., Ma, L., Jie, Z., Chua, T.S.: Temporally grounding natural sentence in video. In: EMNLP. pp. 162–171 (2018)
2018
Earlier work this paper cites.
Lin, T., Zhao, X., Su, H., Wang, C., Yang, M.: Bsn: Boundary sensitive network for temporal action proposal generation. In: ECCV. pp. 3–19 (2018)
2018
Earlier work this paper cites.
Liu, M., Wang, X., Nie, L., Tian, Q., Chen, B., Chua, T.S.: Cross-modal moment localization in videos. In: ACM MM. pp. 843–851 (2018)
2018
Earlier work this paper cites.
Shao, D., Xiong, Y., Zhao, Y., Huang, Q., Qiao, Y., Lin, D.: Find and focus: Retrieve and localize video events with natural language queries. In: ECCV. pp. 200–216 (2018)
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
Feichtenhofer, C., Fan, H., Malik, J., He, K.: Slowfast networks for video recognition. In: ICCV. pp. 6202–6211 (2019)
2019
Earlier work this paper cites.
Ge, R., Gao, J., Chen, K., Nevatia, R.: Mac: Mining activity concepts for language-based temporal localization. In: WACV. pp. 245–253. IEEE (2019)
2019
Earlier work this paper cites.
Ghosh, S., Agarwal, A., Parekh, Z., Hauptmann, A.G.: Excl: Extractive clip localization using natural language descriptions. In: NAACL. pp. 1984–1990 (2019)
2019
Earlier work this paper cites.
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: ICLR (2019)
2019
Earlier work this paper cites.
Lu, C., Chen, L., Tan, C., Li, X., Xiao, J.: Debug: A dense bottom-up grounding approach for natural language video localization. In: EMNLP-IJCNLP. pp. 5144–5153 (2019)
2019
Earlier work this paper cites.
Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. In: CVPR. pp. 658–666 (2019)
2019
Earlier work this paper cites.
Xiong, B., Kalantidis, Y., Ghadiyaram, D., Grauman, K.: Less is more: Learning highlight detection from video duration. In: CVPR. pp. 1258–1267 (2019)
2019
Earlier work this paper cites.
Xu, H., He, K., Plummer, B.A., Sigal, L., Sclaroff, S., Saenko, K.: Multilevel language and vision integration for text-to-clip retrieval. In: AAAI. vol. 33, pp. 9062–9069 (2019)
2019
Earlier work this paper cites.
Yuan, Y., Ma, L., Wang, J., Liu, W., Zhu, W.: Semantic conditioned dynamic modulation for temporal sentence grounding in videos. In: Neurips. vol. 32 (2019)
2019
Cited alongside, same era.
Yuan, Y., Mei, T., Zhu, W.: To find where you talk: Temporal sentence localization in video with attention based location regression. In: AAAI. vol. 33, pp. 9159–9166 (2019)
2019
Cited alongside, same era.
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: CVPR. pp. 1247–1257 (2019)
2019
Cited alongside, same era.
Zhang, S., Su, J., Luo, J.: Exploiting temporal relationships in video moment localization with natural language. In: ACM MM. pp. 1230–1238 (2019)
2019
Cited alongside, same era.
Zhang, Z., Lin, Z., Zhao, Z., Xiao, Z.: Cross-modal interaction networks for query-based moment retrieval in videos. In: ACM SIGIR. pp. 655–664 (2019)
Xiao, S., Chen, L., Zhang, S., Ji, W., Shao, J., Ye, L., Xiao, J.: Boundary proposal network for two-stage natural language video localization. In: AAAI. vol. 35, pp. 2986–2994 (2021)
2021
Later among the works it cites.
Yuan, Y., Lan, X., Wang, X., Chen, L., Wang, Z., Zhu, W.: A closer look at temporal sentence grounding in videos: Dataset and metric. In: Proc. 2nd Int. Workshop on Human-Centric Multimedia Analysis. pp. 13–21 (2021)
2021
Later among the works it cites.
Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: ICLR (2021)
2021
Later among the works it cites.
Hao, J., Sun, H., Ren, P., Wang, J., Qi, Q., Liao, J.: Can shuffling video benefit temporal bias problem: A novel training framework for temporal grounding. In: ECCV. pp. 130–147. Springer (2022)
2022
Later among the works it cites.
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2019
Cited alongside, same era.
Zhu, X., Hu, H., Lin, S., Dai, J.: Deformable convnets v2: More deformable, better results. In: CVPR. pp. 9308–9316 (2019)
2019
Cited alongside, same era.
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: ECCV. pp. 213–229. Springer (2020)
2020
Cited alongside, same era.
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: AAAI. vol. 34, pp. 10551–10558 (2020)
2020
Cited alongside, same era.
Kong, Q., Cao, Y., Iqbal, T., Wang, Y., Wang, W., Plumbley, M.D.: Panns: Large-scale pretrained audio neural networks for audio pattern recognition. IEEE/ACM Trans. on Audio, Speech, and Language Processing 28
2020
Cited alongside, same era.
Lei, J., Yu, L., Berg, T.L., Bansal, M.: Tvr: A large-scale dataset for video-subtitle moment retrieval. In: ECCV. pp. 447–463. Springer (2020)
2020
Cited alongside, same era.
Mun, J., Cho, M., Han, B.: Local-global video-text interactions for temporal grounding. In: CVPR. pp. 10810–10819 (2020)
2020
Cited alongside, same era.
Zeng, R., Xu, H., Huang, W., Chen, P., Tan, M., Gan, C.: Dense regression network for video grounding. In: CVPR. pp. 10287–10296 (2020)
2020
Cited alongside, same era.
Li, F., Zhang, H., Liu, S., Guo, J., Ni, L.M., Zhang, L.: Dn-detr: Accelerate detr training by introducing query denoising. In: CVPR. pp. 13619–13627 (2022)
2022
Later among the works it cites.
Liu, S., Li, F., Zhang, H., Yang, X., Qi, X., Su, H., Zhu, J., Zhang, L.: Dab-detr: Dynamic anchor boxes are better queries for detr. In: ICLR (2022)
2022
Later among the works it cites.
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: CVPR. pp. 3042–3051 (2022)
2022
Later among the works it cites.
Roh, B., Shin, J., Shin, W., Kim, S.: Sparse detr: Efficient end-to-end object detection with learnable sparsity. In: ICLR (2022)
2022
Later among the works it cites.
Wang, Y., Zhang, X., Yang, T., Sun, J.: Anchor detr: Query design for transformer-based detector. In: AAAI. vol. 36, pp. 2567–2575 (2022)
2022
Later among the works it cites.
Wang, Z., Wang, L., Wu, T., Li, T., Wu, G.: Negative sample matters: A renaissance of metric learning for temporal grounding. In: AAAI. vol. 36, pp. 2613–2623 (2022)
2022
Later among the works it cites.
Zhang, G., Luo, Z., Yu, Y., Cui, K., Lu, S.: Accelerating detr convergence via semantic-aligned matching. In: CVPR. pp. 949–958 (2022)
2022
Later among the works it cites.
Jang, J., Park, J., Kim, J., Kwon, H., Sohn, K.: Knowing where to focus: Event-aware transformer for video grounding. In: ICCV. pp. 13846–13856 (2023)
2023
Closest in time.
Lee, P., Kim, T., Shim, M., Wee, D., Byun, H.: Decomposed cross-modal distillation for rgb-based temporal action detection. In: CVPR. pp. 2373–2383 (2023)
2023
Closest in time.
Li, F., Zeng, A., Liu, S., Zhang, H., Li, H., Zhang, L., Ni, L.M.: Lite detr: An interleaved multi-scale encoder for efficient detr. In: CVPR. pp. 18558–18567 (2023)
2023
Closest in time.
Li, H., Cao, M., Cheng, X., Li, Y., Zhu, Z., Zou, Y.: G2l: Semantically aligned and uniform video grounding via geodesic and game theory. In: ICCV. pp. 12032–12042 (2023)
2023
Closest in time.
Li, P., Xie, C.W., Xie, H., Zhao, L., Zhang, L., Zheng, Y., Zhao, D., Zhang, Y.: Momentdiff: Generative video moment retrieval from random to real. In: Neurips (2023)
2023
Closest in time.
Lin, K.Q., Zhang, P., Chen, J., Pramanick, S., Gao, D., Wang, A.J., Yan, R., Shou, M.Z.: Univtg: Towards unified video-language temporal grounding. In: ICCV. pp. 2794–2804 (2023)
2023
Closest in time.
Lin, Y., Yuan, Y., Zhang, Z., Li, C., Zheng, N., Hu, H.: Detr does not need multi-scale or locality design. In: ICCV. pp. 6545–6554 (2023)
2023
Closest in time.
Liu, S., Ren, T., Chen, J., Zeng, Z., Zhang, H., Li, F., Li, H., Huang, J., Su, H., Zhu, J., Zhang, L.: Detection transformer with stable matching. In: ICCV. pp. 6491–6500 (2023)
2023
Closest in time.
Moon, W., Hyun, S., Park, S., Park, D., Heo, J.P.: Query-dependent video representation for moment retrieval and highlight detection. In: CVPR. pp. 23023–23033 (2023)
2023
Closest in time.
Shi, D., Zhong, Y., Cao, Q., Ma, L., Li, J., Tao, D.: Tridet: Temporal action detection with relative boundary modeling. In: CVPR. pp. 18857–18866 (2023)
2023
Closest in time.
Xu, Y., Sun, Y., Li, Y., Shi, Y., Zhu, X., Du, S.: Mh-detr: Video moment and highlight detection with cross-modal transformer. In: ACM MM (2023)
2023
Closest in time.
Yan, S., Xiong, X., Nagrani, A., Arnab, A., Wang, Z., Ge, W., Ross, D., Schmid, C.: Unloc: A unified framework for video localization tasks. In: ICCV. pp. 13623–13633 (2023)
2023
Closest in time.
Ye, M., Ke, L., Li, S., Tai, Y.W., Tang, C.K., Danelljan, M., Yu, F.: Cascade-detr: Delving into high-quality universal object detection. In: ICCV. pp. 6704–6714 (2023)
2023
Closest in time.
Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L., Shum, H.Y.: Dino: Detr with improved denoising anchor boxes for end-to-end object detection. In: ICLR (2023)
2023
Closest in time.
Zheng, D., Dong, W., Hu, H., Chen, X., Wang, Y.: Less is more: Focus attention for efficient detr. In: ICCV. pp. 6674–6683 (2023)
2023
Closest in time.