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Hierarchical Visual-Textual Graph for Temporal Activity Localization via Language

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12365))

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Abstract

Temporal Activity Localization via Language (TALL) in video is a recently proposed challenging vision task, and tackling it requires fine-grained understanding of the video content, however, this is overlooked by most of the existing works. In this paper, we propose a novel TALL method which builds a Hierarchical Visual-Textual Graph to model interactions between the objects and words as well as among the objects to jointly understand the video contents and the language. We also design a convolutional network with cross-channel communication mechanism to further encourage the information passing between the visual and textual modalities. Finally, we propose a loss function that enforces alignment of the visual representation of the localized activity and the sentence representation, so that the model can predict more accurate temporal boundaries. We evaluated our proposed method on two popular benchmark datasets: Charades-STA and ActivityNet Captions, and achieved state-of-the-art performances on both datasets. Code is available at https://github.com/forwchen/HVTG.

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Notes

  1. 1.

    Due to the space limit, more experiments are placed in the Supplementary Material.

References

  1. Anderson, P., et al.: Bottom-up and top-down attention for image captioning and visual question answering. In: CVPR (2018)

    Google Scholar 

  2. Ba, L.J., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)

  3. Buch, S., Escorcia, V., Ghanem, B., Fei-Fei, L., Niebles, J.C.: End-to-end, single-stream temporal action detection in untrimmed videos. In: BMVC (2017)

    Google Scholar 

  4. Buch, S., Escorcia, V., Shen, C., Ghanem, B., Niebles, J.C.: SST: single-stream temporal action proposals. In: CVPR (2017)

    Google Scholar 

  5. Carreira, J., Zisserman, A.: Quo vadis, action recognition? A new model and the kinetics dataset. In: CVPR (2017)

    Google Scholar 

  6. Chen, J., Chen, X., Ma, L., Jie, Z., Chua, T.: Temporally grounding natural sentence in video. In: EMNLP (2018)

    Google Scholar 

  7. Chen, S., Jiang, Y.: Semantic proposal for activity localization in videos via sentence query. In: AAAI (2019)

    Google Scholar 

  8. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS Workshop on Deep Learning (2014)

    Google Scholar 

  9. Dai, X., Singh, B., Zhang, G., Davis, L.S., Chen, Y.Q.: Temporal context network for activity localization in videos. In: ICCV (2017)

    Google Scholar 

  10. Escorcia, V., Caba Heilbron, F., Niebles, J.C., Ghanem, B.: DAPs: deep action proposals for action understanding. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 768–784. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_47

    Chapter  Google Scholar 

  11. Faghri, F., Fleet, D.J., Kiros, J.R., Fidler, S.: VSE++: improving visual-semantic embeddings with hard negatives. In: BMVC (2018)

    Google Scholar 

  12. Gao, J., Sun, C., Yang, Z., Nevatia, R.: TALL: temporal activity localization via language query. In: ICCV (2017)

    Google Scholar 

  13. Ge, R., Gao, J., Chen, K., Nevatia, R.: MAC: mining activity concepts for language-based temporal localization. In: WACV (2019)

    Google Scholar 

  14. Hahn, M., Kadav, A., Rehg, J.M., Graf, H.P.: Tripping through time: efficient localization of activities in videos. arXiv preprint arXiv:1904.09936 (2019)

  15. He, K., Gkioxari, G., Dollár, P., Girshick, R.B.: Mask R-CNN. In: ICCV (2017)

    Google Scholar 

  16. Hendricks, L.A., Wang, O., Shechtman, E., Sivic, J., Darrell, T., Russell, B.C.: Localizing moments in video with natural language. In: ICCV (2017)

    Google Scholar 

  17. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  18. Jiang, B., Huang, X., Yang, C., Yuan, J.: Cross-modal video moment retrieval with spatial and language-temporal attention. In: ICMR (2019)

    Google Scholar 

  19. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)

    Google Scholar 

  20. Krishna, R., Hata, K., Ren, F., Fei-Fei, L., Niebles, J.C.: Dense-captioning events in videos. In: ICCV (2017)

    Google Scholar 

  21. Krishna, R., et al.: Visual genome: connecting language and vision using crowdsourced dense image annotations. Int. J. Comput. Vis. 123(1), 32–73 (2017). https://doi.org/10.1007/s11263-016-0981-7

    Article  MathSciNet  Google Scholar 

  22. Lin, D., Fidler, S., Kong, C., Urtasun, R.: Visual semantic search: retrieving videos via complex textual queries. In: CVPR (2014)

    Google Scholar 

  23. Lin, T., Zhao, X., Shou, Z.: Single shot temporal action detection. In: ACM MM (2017)

    Google Scholar 

  24. Liu, M., Wang, X., Nie, L., He, X., Chen, B., Chua, T.: Attentive moment retrieval in videos. In: ACM SIGIR (2018)

    Google Scholar 

  25. Liu, M., Wang, X., Nie, L., Tian, Q., Chen, B., Chua, T.: Cross-modal moment localization in videos. In: ACM MM (2018)

    Google Scholar 

  26. 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 (2019)

    Google Scholar 

  27. Mettes, P., van Gemert, J.C., Cappallo, S., Mensink, T., Snoek, C.G.M.: Bag-of-fragments: selecting and encoding video fragments for event detection and recounting. In: ICMR (2015)

    Google Scholar 

  28. Miech, A., Laptev, I., Sivic, J.: Learning a text-video embedding from incomplete and heterogeneous data. arXiv preprint arXiv:1804.02516 (2018)

  29. Mithun, N.C., Li, J., Metze, F., Roy-Chowdhury, A.K.: Learning joint embedding with multimodal cues for cross-modal video-text retrieval. In: ICMR (2018)

    Google Scholar 

  30. Otani, M., Nakashima, Y., Rahtu, E., Heikkilä, J., Yokoya, N.: Learning joint representations of videos and sentences with web image search. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9913, pp. 651–667. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46604-0_46

    Chapter  Google Scholar 

  31. Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: EMNLP (2014)

    Google Scholar 

  32. Pont-Tuset, J., Arbelaez, P., Barron, J.T., Marqués, F., Malik, J.: Multiscale combinatorial grouping for image segmentation and object proposal generation. IEEE TPAMI 39(1), 128–140 (2017)

    Article  Google Scholar 

  33. Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS (2015)

    Google Scholar 

  34. Shou, Z., Chan, J., Zareian, A., Miyazawa, K., Chang, S.: CDC: convolutional-de-convolutional networks for precise temporal action localization in untrimmed videos. In: CVPR (2017)

    Google Scholar 

  35. Shou, Z., Wang, D., Chang, S.: Temporal action localization in untrimmed videos via multi-stage CNNs. In: CVPR (2016)

    Google Scholar 

  36. Sigurdsson, G.A., Varol, G., Wang, X., Farhadi, A., Laptev, I., Gupta, A.: Hollywood in homes: crowdsourcing data collection for activity understanding. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 510–526. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_31

    Chapter  Google Scholar 

  37. Song, X., Han, Y.: VAL: visual-attention action localizer. In: PCM (2018)

    Google Scholar 

  38. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-ResNet and the impact of residual connections on learning. In: AAAI (2017)

    Google Scholar 

  39. Tran, D., Bourdev, L.D., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: ICCV (2015)

    Google Scholar 

  40. Ulyanov, D., Vedaldi, A., Lempitsky, V.S.: Instance normalization: the missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016)

  41. Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: ICLR (2018)

    Google Scholar 

  42. Wang, J., Ma, L., Jiang, W.: Temporally grounding language queries in videos by contextual boundary-aware prediction. In: AAAI (2020)

    Google Scholar 

  43. Wang, W., Huang, Y., Wang, L.: Language-driven temporal activity localization: a semantic matching reinforcement learning model. In: CVPR (2019)

    Google Scholar 

  44. Wang, X., Gupta, A.: Videos as space-time region graphs. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 413–431. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01228-1_25

    Chapter  Google Scholar 

  45. Wray, M., Larlus, D., Csurka, G., Damen, D.: Fine-grained action retrieval through multiple parts-of-speech embeddings. arXiv preprint arXiv:1908.03477 (2019)

  46. Xu, B., Wang, N., Chen, T., Li, M.: Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv:1505.00853 (2015)

  47. Xu, H., Das, A., Saenko, K.: R-C3D: region convolutional 3D network for temporal activity detection. In: ICCV (2017)

    Google Scholar 

  48. 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 (2019)

    Google Scholar 

  49. Xu, H., He, K., Sigal, L., Sclaroff, S., Saenko, K.: Text-to-clip video retrieval with early fusion and re-captioning. arXiv preprint arXiv:1804.05113 (2018)

  50. Xu, R., Xiong, C., Chen, W., Corso, J.J.: Jointly modeling deep video and compositional text to bridge vision and language in a unified framework. In: AAAI (2015)

    Google Scholar 

  51. Yang, J., Ren, Z., Gan, C., Zhu, H., Parikh, D.: Cross-channel communication networks. In: NeurIPS (2019)

    Google Scholar 

  52. Yu, Y., Kim, J., Kim, G.: A joint sequence fusion model for video question answering and retrieval. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 487–503. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_29

    Chapter  Google Scholar 

  53. Yuan, Y., Mei, T., Zhu, W.: To find where you talk: temporal sentence localization in video with attention based location regression. In: AAAI (2019)

    Google Scholar 

  54. Zhang, B., Hu, H., Sha, F.: Cross-modal and hierarchical modeling of video and text. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 385–401. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_23

    Chapter  Google Scholar 

  55. Zhang, D., Dai, X., Wang, X., Wang, Y.: S3D: single shot multi-span detector via fully 3D convolutional networks. In: BMVC (2018)

    Google Scholar 

  56. Zhang, D., Dai, X., Wang, X., Wang, Y., Davis, L.S.: MAN: moment alignment network for natural language moment retrieval via iterative graph adjustment. In: CVPR (2019)

    Google Scholar 

  57. Zhao, Y., Xiong, Y., Wang, L., Wu, Z., Tang, X., Lin, D.: Temporal action detection with structured segment networks. In: ICCV (2017)

    Google Scholar 

  58. Zheng, Y., Pal, D.K., Savvides, M.: Ring loss: convex feature normalization for face recognition. In: CVPR (2018)

    Google Scholar 

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Authors and Affiliations

  1. Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China

    Shaoxiang Chen & Yu-Gang Jiang

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  1. Shaoxiang Chen
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  2. Yu-Gang Jiang
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Correspondence to Yu-Gang Jiang .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Chen, S., Jiang, YG. (2020). Hierarchical Visual-Textual Graph for Temporal Activity Localization via Language. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12365. Springer, Cham. https://doi.org/10.1007/978-3-030-58565-5_36

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  • DOI: https://doi.org/10.1007/978-3-030-58565-5_36

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