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Audio-Visual Mismatch-Aware Video Retrieval via Association and Adjustment

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13674))

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Abstract

Retrieving desired videos using natural language queries has attracted increasing attention in research and industry fields as a huge number of videos appear on the internet. Some existing methods attempted to address this video retrieval problem by exploiting multi-modal information, especially audio-visual data of videos. However, many videos often have mismatched visual and audio cues for several reasons including background music, noise, and even missing sound. Therefore, the naive fusion of such mismatched visual and audio cues can negatively affect the semantic embedding of video scenes. Mismatch condition can be categorized into two cases: (i) Audio itself does not exist (ii) Audio exists but does not match with visual. To deal with (i), we introduce audio-visual associative memory (AVA-Memory) to associate audio cues even from videos without audio data. The associated audio cues can guide the video embedding feature to be aware of audio information even in the missing audio condition. To address (ii), we propose audio embedding adjustment by considering the degree of matching between visual and audio data. In this procedure, constructed AVA-Memory enables to figure out how well the visual and audio in the video are matched and to adjust the weighting between actual audio and associated audio. Experimental results show that the proposed method outperforms other state-of-the-art video retrieval methods. Further, we validate the effectiveness of the proposed network designs with ablation studies and analyses.

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References

  1. Bain, M., Nagrani, A., Varol, G., Zisserman, A.: Frozen in time: A joint video and image encoder for end-to-end retrieval. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 1728–1738 (2021)

    Google Scholar 

  2. Cai, Q., Pan, Y., Yao, T., Yan, C., Mei, T.: Memory matching networks for one-shot image recognition. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4080–4088 (2018)

    Google Scholar 

  3. Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6299–6308 (2017)

    Google Scholar 

  4. Chen, J., Hu, H., Wu, H., Jiang, Y., Wang, C.: Learning the best pooling strategy for visual semantic embedding. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15789–15798 (2021)

    Google Scholar 

  5. Chen, S., Zhao, Y., Jin, Q., Wu, Q.: Fine-grained video-text retrieval with hierarchical graph reasoning. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10638–10647 (2020)

    Google Scholar 

  6. Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning (ICML), pp. 1597–1607. PMLR (2020)

    Google Scholar 

  7. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT) (2019)

    Google Scholar 

  8. Dong, J., Li, X., Snoek, C.G.: Predicting visual features from text for image and video caption retrieval. IEEE Trans. Multimedia 20(12), 3377–3388 (2018)

    Article  Google Scholar 

  9. Dong, J., et al.: Dual encoding for zero-example video retrieval. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9346–9355 (2019)

    Google Scholar 

  10. Dong, J., et al.: Dual encoding for video retrieval by text. IEEE Trans. Pattern Anal. Mach. Intell. 44, 4065–4080 (2021)

    Google Scholar 

  11. Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations (ICLR) (2020)

    Google Scholar 

  12. Dzabraev, M., Kalashnikov, M., Komkov, S., Petiushko, A.: Mdmmt: Multidomain multimodal transformer for video retrieval. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3354–3363 (2021)

    Google Scholar 

  13. Faghri, F., Fleet, D.J., Kiros, J.R., Fidler, S.: Vse++: Improving visual-semantic embeddings with hard negatives. In: British Machine Vision Conference (BMVC) (2018)

    Google Scholar 

  14. Francis, D., Anh Nguyen, P., Huet, B., Ngo, C.W.: Fusion of multimodal embeddings for ad-hoc video search. In: IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) (2019)

    Google Scholar 

  15. Fu, Z., Liu, Q., Fu, Z., Wang, Y.: Stmtrack: Template-free visual tracking with space-time memory networks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13774–13783 (2021)

    Google Scholar 

  16. Gabeur, V., Nagrani, A., Sun, C., Alahari, K., Schmid, C.: Masking modalities for cross-modal video retrieval. In: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 1766–1775 (2022)

    Google Scholar 

  17. Gabeur, V., Sun, C., Alahari, K., Schmid, C.: Multi-modal transformer for video retrieval. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 214–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58548-8_13

    Chapter  Google Scholar 

  18. Gong, D., et al.: Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 1705–1714 (2019)

    Google Scholar 

  19. Han, T., Xie, W., Zisserman, A.: Memory-augmented dense predictive coding for video representation learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 312–329. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58580-8_19

    Chapter  Google Scholar 

  20. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)

    Google Scholar 

  21. Hu, P., Peng, X., Zhu, H., Zhen, L., Lin, J.: Learning cross-modal retrieval with noisy labels. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5403–5413 (2021)

    Google Scholar 

  22. Huang, Y., Wang, L.: Acmm: Aligned cross-modal memory for few-shot image and sentence matching. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 5774–5783 (2019)

    Google Scholar 

  23. Kaiser, Ł., Nachum, O., Roy, A., Bengio, S.: Learning to remember rare events. In: International Conference on Learning Representations (ICLR) (2017)

    Google Scholar 

  24. Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3128–3137 (2015)

    Google Scholar 

  25. Kim, J.U., Park, S., Ro, Y.M.: Robust small-scale pedestrian detection with cued recall via memory learning. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3050–3059 (2021)

    Google Scholar 

  26. Kim, M., Hong, J., Park, S.J., Ro, Y.M.: Multi-modality associative bridging through memory: Speech sound recollected from face video. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 296–306 (2021)

    Google Scholar 

  27. Kingma, D., Ba, J.: Adam: A method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2015)

    Google Scholar 

  28. Kiros, R., Salakhutdinov, R., Zemel, R.S.: Unifying visual-semantic embeddings with multimodal neural language models. arXiv preprint arXiv:1411.2539 (2014)

  29. Lee, S., Kim, H.G., Choi, D.H., Kim, H.I., Ro, Y.M.: Video prediction recalling long-term motion context via memory alignment learning. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3054–3063 (2021)

    Google Scholar 

  30. Lee, S., Kim, H.I., Ro, Y.M.: Weakly paired associative learning for sound and image representations via bimodal associative memory. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10534–10543 (2022)

    Google Scholar 

  31. Li, X., Xu, C., Yang, G., Chen, Z., Dong, J.: W2VV++: fully deep learning for ad-hoc video search. In: ACM International Conference on Multimedia (ACM MM), pp. 1786–1794 (2019)

    Google Scholar 

  32. Li, X., Zhou, F., Xu, C., Ji, J., Yang, G.: Sea: Sentence encoder assembly for video retrieval by textual queries. IEEE Trans. Multimedia 23, 4351–4362 (2021)

    Article  Google Scholar 

  33. Li, Y., et al.: Tgif: A new dataset and benchmark on animated gif description. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4641–4650 (2016)

    Google Scholar 

  34. Liu, H., Luo, R., Shang, F., Niu, M., Liu, Y.: Progressive semantic matching for video-text retrieval. In: ACM International Conference on Multimedia (ACM MM), pp. 5083–5091 (2021)

    Google Scholar 

  35. Liu, S., Fan, H., Qian, S., Chen, Y., Ding, W., Wang, Z.: Hit: Hierarchical transformer with momentum contrast for video-text retrieval. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 11915–11925 (2021)

    Google Scholar 

  36. Liu, Y., Albanie, S., Nagrani, A., Zisserman, A.: Use what you have: Video retrieval using representations from collaborative experts. In: British Machine Vision Conference (BMVC) (2019)

    Google Scholar 

  37. Marchetti, F., Becattini, F., Seidenari, L., Bimbo, A.D.: Mantra: Memory augmented networks for multiple trajectory prediction. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7143–7152 (2020)

    Google Scholar 

  38. Miech, A., Alayrac, J.B., Laptev, I., Sivic, J., Zisserman, A.: Thinking fast and slow: Efficient text-to-visual retrieval with transformers. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9826–9836 (2021)

    Google Scholar 

  39. Miech, A., Alayrac, J.B., Smaira, L., Laptev, I., Sivic, J., Zisserman, A.: End-to-end learning of visual representations from uncurated instructional videos. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9879–9889 (2020)

    Google Scholar 

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

  41. Miech, A., Zhukov, D., Alayrac, J.B., Tapaswi, M., Laptev, I., Sivic, J.: Howto100m: Learning a text-video embedding by watching hundred million narrated video clips. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 2630–2640 (2019)

    Google Scholar 

  42. Mithun, N.C., Li, J., Metze, F., Roy-Chowdhury, A.K.: Learning joint embedding with multimodal cues for cross-modal video-text retrieval. In: ACM International Conference on Multimedia Retrieval (ICMR), pp. 19–27 (2018)

    Google Scholar 

  43. 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 

  44. Park, H., Noh, J., Ham, B.: Learning memory-guided normality for anomaly detection. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14372–14381 (2020)

    Google Scholar 

  45. Patrick, M., et al.: Support-set bottlenecks for video-text representation learning. In: International Conference on Learning Representations (ICLR) (2020)

    Google Scholar 

  46. Song, Y., Soleymani, M.: Polysemous visual-semantic embedding for cross-modal retrieval. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1979–1988 (2019)

    Google Scholar 

  47. Torabi, A., Tandon, N., Sigal, L.: Learning language-visual embedding for movie understanding with natural-language. arXiv preprint arXiv:1609.08124 (2016)

  48. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 5998–6008 (2017)

    Google Scholar 

  49. Wang, W., Gao, J., Yang, X., Xu, C.: Learning coarse-to-fine graph neural networks for video-text retrieval. IEEE Trans. Multimedia 23, 2386–2397 (2021)

    Article  Google Scholar 

  50. Wang, X., Zhu, L., Yang, Y.: T2vlad: global-local sequence alignment for text-video retrieval. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5079–5088 (2021)

    Google Scholar 

  51. Wang, X., Wu, J., Chen, J., Li, L., Wang, Y.F., Wang, W.Y.: Vatex: A large-scale, high-quality multilingual dataset for video-and-language research. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 4581–4591 (2019)

    Google Scholar 

  52. Wei, J., Xu, X., Yang, Y., Ji, Y., Wang, Z., Shen, H.T.: Universal weighting metric learning for cross-modal matching. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13005–13014 (2020)

    Google Scholar 

  53. Wei, J., Yang, Y., Xu, X., Zhu, X., Shen, H.T.: Universal weighting metric learning for cross-modal retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 44, 6534–6545 (2021)

    Google Scholar 

  54. Wray, M., Larlus, D., Csurka, G., Damen, D.: Fine-grained action retrieval through multiple parts-of-speech embeddings. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 450–459 (2019)

    Google Scholar 

  55. Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1492–1500 (2017)

    Google Scholar 

  56. Xu, J., Mei, T., Yao, T., Rui, Y.: Msr-vtt: A large video description dataset for bridging video and language. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5288–5296 (2016)

    Google Scholar 

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

    Google Scholar 

  58. Yang, T., Chan, A.B.: Learning dynamic memory networks for object tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 153–169. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01240-3_10

    Chapter  Google Scholar 

  59. Yang, X., Dong, J., Cao, Y., Wang, X., Wang, M., Chua, T.S.: Tree-augmented cross-modal encoding for complex-query video retrieval. In: International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR), pp. 1339–1348 (2020)

    Google Scholar 

  60. 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 

  61. 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 

  62. Zhu, L., Yang, Y.: Inflated episodic memory with region self-attention for long-tailed visual recognition. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4344–4353 (2020)

    Google Scholar 

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Acknowledgement

This work was supported by IITP grant(No. 2020-0-00004).

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Authors and Affiliations

  1. Image and Video Systems Lab, KAIST, Daejeon, South Korea

    Sangmin Lee, Sungjune Park & Yong Man Ro

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  1. Sangmin Lee
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Correspondence to Sangmin Lee .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Lee, S., Park, S., Ro, Y.M. (2022). Audio-Visual Mismatch-Aware Video Retrieval via Association and Adjustment. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13674. Springer, Cham. https://doi.org/10.1007/978-3-031-19781-9_29

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