Skip to main content
SpringerLink
Log in

Video Graph Transformer for Video Question Answering

  • Conference paper
  • First Online:
Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13696))

Included in the following conference series:

Abstract

This paper proposes a Video Graph Transformer (VGT) model for Video Question Answering (VideoQA). VGT’s uniqueness are two-fold: 1) it designs a dynamic graph transformer module which encodes video by explicitly capturing the visual objects, their relations, and dynamics for complex spatio-temporal reasoning; and 2) it exploits disentangled video and text Transformers for relevance comparison between the video and text to perform QA, instead of entangled cross-modal Transformer for answer classification. Vision-text communication is done by additional cross-modal interaction modules. With more reasonable video encoding and QA solution, we show that VGT can achieve much better performances on VideoQA tasks that challenge dynamic relation reasoning than prior arts in the pretraining-free scenario. Its performances even surpass those models that are pretrained with millions of external data. We further show that VGT can also benefit a lot from self-supervised cross-modal pretraining, yet with orders of magnitude smaller data. These results clearly demonstrate the effectiveness and superiority of VGT, and reveal its potential for more data-efficient pretraining. With comprehensive analyses and some heuristic observations, we hope that VGT can promote VQA research beyond coarse recognition/description towards fine-grained relation reasoning in realistic videos. Our code is available at https://github.com/sail-sg/VGT.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The model demands on less training data to achieve good performance.

  2. 2.

    We assume that the group of objects do not change in a short video clip.

References

  1. Amrani, E., Ben-Ari, R., Rotman, D., Bronstein, A.: Noise estimation using density estimation for self-supervised multimodal learning. In: AAAI Conference on Artificial Intelligence (AAAI), vol. 35, pp. 6644–6652 (2021)

    Google Scholar 

  2. Anderson, P., et al.: Bottom-up and top-down attention for image captioning and visual question answering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6077–6086 (2018)

    Google Scholar 

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

    Google Scholar 

  4. Battaglia, P.W., et al.: Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261 (2018)

  5. Bertasius, G., Wang, H., Torresani, L.: Is space-time attention all you need for video understanding? In: ICML, pp. 813–824. PMLR (2021)

    Google Scholar 

  6. Buch, S., Eyzaguirre, C., Gaidon, A., Wu, J., Fei-Fei, L., Niebles, J.C.: Revisiting the “video” in video-language understanding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2917–2927 (2022)

    Google Scholar 

  7. Chen, X., et al.: Microsoft coco captions: data collection and evaluation server. arXiv preprint arXiv:1504.00325 (2015)

  8. Chen, Y.C., et al.: UNITER: UNiversal image-TExt representation learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12375, pp. 104–120. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58577-8_7

    Chapter  Google Scholar 

  9. Cherian, A., Hori, C., Marks, T.K., Le Roux, J.: (2.5+ 1) d spatio-temporal scene graphs for video question answering. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 444–453 (2022)

    Google Scholar 

  10. Dang, L.H., Le, T.M., Le, V., Tran, T.: Hierarchical object-oriented spatio-temporal reasoning for video question answering. In: IJCAI (2021)

    Google Scholar 

  11. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: NAACL (2019)

    Google Scholar 

  12. Ding, D., Hill, F., Santoro, A., Reynolds, M., Botvinick, M.: Attention over learned object embeddings enables complex visual reasoning. Adv. Neural Inf. Process. syst. (NeurIPS) 34, 9112–9124 (2021)

    Google Scholar 

  13. Dosovitskiy, A., et al.: An image is worth \(16\times 16\) words: transformers for image recognition at scale. In: International Conference on Representation Learning (ICLR) (2020)

    Google Scholar 

  14. Fan, C., Zhang, X., Zhang, S., Wang, W., Zhang, C., Huang, H.: Heterogeneous memory enhanced multimodal attention model for video question answering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1999–2007 (2019)

    Google Scholar 

  15. Fu, T.J., et al.: Violet: end-to-end video-language transformers with masked visual-token modeling. In: arXiv preprint arXiv:2111.12681 (November 2021)

  16. Gao, J., Ge, R., Chen, K., Nevatia, R.: Motion-appearance co-memory networks for video question answering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6576–6585 (2018)

    Google Scholar 

  17. Geng, S., et al.: Dynamic graph representation learning for video dialog via multi-modal shuffled transformers. In: AAAI Conference on Artificial Intelligence (AAAI) (2021)

    Google Scholar 

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

    Google Scholar 

  19. Huang, D., Chen, P., Zeng, R., Du, Q., Tan, M., Gan, C.: Location-aware graph convolutional networks for video question answering. In: AAAI Conference on Artificial Intelligence (AAAI), vol. 34, pp. 11021–11028 (2020)

    Google Scholar 

  20. Jang, Y., Song, Y., Yu, Y., Kim, Y., Kim, G.: TGIF-QA: toward spatio-temporal reasoning in visual question answering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2758–2766 (2017)

    Google Scholar 

  21. Jia, C., et al.: Scaling up visual and vision-language representation learning with noisy text supervision. In: ICML. pp, 4904–4916. PMLR (2021)

    Google Scholar 

  22. Jiang, J., Chen, Z., Lin, H., Zhao, X., Gao, Y.: Divide and conquer: question-guided spatio-temporal contextual attention for video question answering. In: AAAI Conference on Artificial Intelligence (AAAI), vol. 34, pp. 11101–11108 (2020)

    Google Scholar 

  23. Jiang, P., Han, Y.: Reasoning with heterogeneous graph alignment for video question answering. In: AAAI Conference on Artificial Intelligence (AAAI) (2020)

    Google Scholar 

  24. 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)

    Google Scholar 

  25. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Representation Learning (ICLR) (2017)

    Google Scholar 

  26. Krishna, R., Chami, I., Bernstein, M., Fei-Fei, L.: Referring relationships. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6867–6876 (2018)

    Google Scholar 

  27. Krishna, R., et al.: Visual genome: connecting language and vision using crowdsourced dense image annotations. IJCV 123(1), 32–73 (2017). https://doi.org/10.1007/S11263-016-0981-7

    Article  MathSciNet  Google Scholar 

  28. Le, T.M., Le, V., Venkatesh, S., Tran, T.: Hierarchical conditional relation networks for video question answering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9972–9981 (2020)

    Google Scholar 

  29. Lei, J., et al.: Less is more: ClipBERT for video-and-language learning via sparse sampling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7331–7341 (2021)

    Google Scholar 

  30. Lei, J., Yu, L., Bansal, M., Berg, T.L.: TVQA: localized, compositional video question answering. In: Empirical Methods in Natural Language Processing (EMNLP) (2018)

    Google Scholar 

  31. Li, J., Selvaraju, R., Gotmare, A., Joty, S., Xiong, C., Hoi, S.C.H.: Align before fuse: Vision and language representation learning with momentum distillation. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 34 (2021)

    Google Scholar 

  32. Li, L., Chen, Y.C., Cheng, Y., Gan, Z., Yu, L., Liu, J.: Hero: hierarchical encoder for video+ language omni-representation pre-training. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2046–2065 (2020)

    Google Scholar 

  33. Li, X., et al.: Beyond RNNs: positional self-attention with co-attention for video question answering. In: AAAI Conference on Artificial Intelligence (AAAI), pp. 8658–8665 (2019)

    Google Scholar 

  34. Li, X., et al.: OSCAR: object-semantics aligned pre-training for vision-language tasks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12375, pp. 121–137. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58577-8_8

    Chapter  Google Scholar 

  35. Li, Y., Wang, X., Xiao, J., Ji, W., Chua, T.S.: Invariant grounding for video question answering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2928–2937 (2022)

    Google Scholar 

  36. Liu, F., Liu, J., Wang, W., Lu, H.: Hair: hierarchical visual-semantic relational reasoning for video question answering. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 1698–1707 (2021)

    Google Scholar 

  37. Liu, Z., et al.: Video swin transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3202–3211 (2022)

    Google Scholar 

  38. Lu, J., Batra, D., Parikh, D., Lee, S.: ViLBERT: pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 13–23 (2019)

    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: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9879–9889 (2020)

    Google Scholar 

  40. 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: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 2630–2640 (2019)

    Google Scholar 

  41. Park, J., Lee, J., Sohn, K.: Bridge to answer: structure-aware graph interaction network for video question answering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15526–15535 (2021)

    Google Scholar 

  42. Peng, L., Yang, S., Bin, Y., Wang, G.: Progressive graph attention network for video question answering. In: ACM MM, pp. 2871–2879 (2021)

    Google Scholar 

  43. Peng, M., Wang, C., Gao, Y., Shi, Y., Zhou, X.D.: Multilevel hierarchical network with multiscale sampling for video question answering. In: IJCAI (2022)

    Google Scholar 

  44. Radford, A., et al.: Learning transferable visual models from natural language supervision. In: ICML, pp. 8748–8763. PMLR (2021)

    Google Scholar 

  45. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. (NeurIPS) 28, 1–9 (2015)

    Google Scholar 

  46. Sanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. In: 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing - NeurIPS 2019

    Google Scholar 

  47. Seo, A., Kang, G.C., Park, J., Zhang, B.T.: Attend what you need: motion-appearance synergistic networks for video question answering. In: ACL, pp. 6167–6177 (2021)

    Google Scholar 

  48. Seo, P.H., Nagrani, A., Schmid, C.: Look before you speak: visually contextualized utterances. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16877–16887 (2021)

    Google Scholar 

  49. Shang, X., Di, D., Xiao, J., Cao, Y., Yang, X., Chua, T.S.: Annotating objects and relations in user-generated videos. In: Proceedings of the 2019 on International Conference on Multimedia Retrieval (ICMR), pp. 279–287 (2019)

    Google Scholar 

  50. Shang, X., Xiao, J., Di, D., Chua, T.S.: Relation understanding in videos: a grand challenge overview. In: Proceedings of the 27th ACM International Conference on Multimedia (MM), pp. 2652–2656 (2019)

    Google Scholar 

  51. Sharma, P., Ding, N., Goodman, S., Soricut, R.: Conceptual captions: a cleaned, hypernymed, image alt-text dataset for automatic image captioning. In: ACL, pp. 2556–2565 (2018)

    Google Scholar 

  52. Su, W., et al.: VL-BERT: pre-training of generic visual-linguistic representations. In: International Conference on Representation Learning (ICLR) (2020)

    Google Scholar 

  53. Sun, C., Myers, A., Vondrick, C., Murphy, K., Schmid, C.: VideoBERT: a joint model for video and language representation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 7464–7473 (2019)

    Google Scholar 

  54. Tan, H., Bansal, M.: LXMERT: learning cross-modality encoder representations from transformers. In: EMNLP, pp. 5100–5111 (2019)

    Google Scholar 

  55. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 30 (2017)

    Google Scholar 

  56. Wang, L., et al.: TCL: transformer-based dynamic graph modelling via contrastive learning. arXiv preprint arXiv:2105.07944 (2021)

  57. Wang, X., Gupta, A.: Videos as space-time region graphs. In: European Conference on Computer Vision (ECCV), pp. 399–417 (2018)

    Google Scholar 

  58. Xiao, J., Shang, X., Yang, X., Tang, S., Chua, T.-S.: Visual relation grounding in videos. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12351, pp. 447–464. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58539-6_27

    Chapter  Google Scholar 

  59. Xiao, J., Shang, X., Yao, A., Chua, T.S.: Next-QA: next phase of question-answering to explaining temporal actions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9777–9786 (2021)

    Google Scholar 

  60. Xiao, J., Yao, A., Liu, Z., Li, Y., Ji, W., Chua, T.S.: Video as conditional graph hierarchy for multi-granular question answering. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pp. 2804–2812 (2022)

    Google Scholar 

  61. Xie, S., Sun, C., Huang, J., Tu, Z., Murphy, K.: Rethinking spatiotemporal feature learning: speed-accuracy trade-offs in video classification. In: European Conference on Computer Vision (ECCV), pp. 305–321 (2018)

    Google Scholar 

  62. Xu, D., et al.: Video question answering via gradually refined attention over appearance and motion. In: ACM MM, pp. 1645–1653 (2017)

    Google Scholar 

  63. Xu, H., et al.: Videoclip: contrastive pre-training for zero-shot video-text understanding. In: EMNLP, pp. 6787–6800 (2021)

    Google Scholar 

  64. Yang, A., Miech, A., Sivic, J., Laptev, I., Schmid, C.: Just ask: learning to answer questions from millions of narrated videos. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 1686–1697 (2021)

    Google Scholar 

  65. Yi, K., et al.: CLEVRER: collision events for video representation and reasoning. In: International Conference on Learning Representations (ICLR) (2019)

    Google Scholar 

  66. Ying, C., et al.: Do transformers really perform badly for graph representation? Adv. Neural Inf. Process. Syst. (NeurIPS) 34, 28877–28888 (2021)

    Google Scholar 

  67. Yu, W., et al.: Learning from inside: self-driven siamese sampling and reasoning for video question answering. Adv. Neural Inf. Process. Syst. (NeurIPS) 34, 26462–26474 (2021)

    Google Scholar 

  68. Yu, Y., Kim, J., Kim, G.: A joint sequence fusion model for video question answering and retrieval. In: European Conference on Computer Vision (ECCV), pp. 471–487 (2018)

    Google Scholar 

  69. Yun, S., Jeong, M., Kim, R., Kang, J., Kim, H.J.: Graph transformer networks. Adv. Neural Inf. Process. Syst. (NeurIPS) 32, 1–11 (2019)

    Google Scholar 

  70. Zellers, R., et al.: Merlot: multimodal neural script knowledge models. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 34 (2021)

    Google Scholar 

  71. Zhong, Y., Ji, W., Xiao, J., Li, Y., Deng, W., Chua, T.S.: Video question answering: datasets, algorithms and challenges. arXiv preprint arXiv:2203.01225 (2022)

  72. Zhu, L., Yang, Y.: ActBERT: learning global-local video-text representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8746–8755 (2020)

    Google Scholar 

  73. Zoph, B., et al.: Rethinking pre-training and self-training. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 3833–3845 (2020)

    Google Scholar 

Download references

Acknowledgements

This research is supported by the Sea-NExT joint Lab. Major work was done when Junbin was a research intern at Sea AI Lab. We greatly thank Angela Yao as well as the anonymous reviewers for their thoughtful comments towards a better work.

Author information

Authors and Affiliations

  1. Sea AI Lab, Singapore, Singapore

    Junbin Xiao, Pan Zhou & Shuicheng Yan

  2. Sea-NExT Joint Lab, Singapore, Singapore

    Junbin Xiao & Tat-Seng Chua

  3. Department of Computer Science, National University of Singapore, Singapore, Singapore

    Junbin Xiao & Tat-Seng Chua

Authors
  1. Junbin Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tat-Seng Chua
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shuicheng Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tat-Seng Chua .

Editor information

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

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 289 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xiao, J., Zhou, P., Chua, TS., Yan, S. (2022). Video Graph Transformer for Video Question Answering. 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 13696. Springer, Cham. https://doi.org/10.1007/978-3-031-20059-5_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20059-5_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20058-8

  • Online ISBN: 978-3-031-20059-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation