Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Amplifying Key Cues for Human-Object-Interaction Detection

  • Conference paper
  • First Online:
Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12359))

Included in the following conference series:

Abstract

Human-object interaction (HOI) detection aims to detect and recognise how people interact with the objects that surround them. This is challenging as different interaction categories are often distinguished only by very subtle visual differences in the scene. In this paper we introduce two methods to amplify key cues in the image, and also a method to combine these and other cues when considering the interaction between a human and an object. First, we introduce an encoding mechanism for representing the fine-grained spatial layout of the human and object (a subtle cue) and also semantic context (a cue, represented by text embeddings of surrounding objects). Second, we use plausible future movements of humans and objects as a cue to constrain the space of possible interactions. Third, we use a gate and memory architecture as a fusion module to combine the cues. We demonstrate that these three improvements lead to a performance which exceeds prior HOI methods across standard benchmarks by a considerable margin.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Similar content being viewed by others

Notes

  1. 1.

    Empirically, we observe the feature ordering to GRU is not sensitive to the HOI detection performance. So we use this order in all experiments.

References

  1. Adam, A., Rivlin, E., Shimshoni, I., Reinitz, D.: Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 555–560 (2008)

    Article  Google Scholar 

  2. Arandjelovic, R., Gronat, P., Torii, A., Pajdla, T., Sivic, J.: NetVLAD: CNN architecture for weakly supervised place recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5297–5307 (2016)

    Google Scholar 

  3. Chao, Y.W., Wang, Z., He, Y., Wang, J., Deng, J.: HICO: a benchmark for recognizing human-object interactions in images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1017–1025 (2015)

    Google Scholar 

  4. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)

  5. Dai, B., Zhang, Y., Lin, D.: Detecting visual relationships with deep relational networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3076–3086 (2017)

    Google Scholar 

  6. Fang, H.-S., Cao, J., Tai, Y.-W., Lu, C.: Pairwise body-part attention for recognizing human-object interactions. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 52–68. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01249-6_4

    Chapter  Google Scholar 

  7. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)

    Article  Google Scholar 

  8. Fouhey, D.F., Zitnick, C.L.: Predicting object dynamics in scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2019–2026 (2014)

    Google Scholar 

  9. Gao, C., Zou, Y., Huang, J.B.: iCAN: instance-centric attention network for human-object interaction detection. In: British Machine Vision Conference (2018)

    Google Scholar 

  10. Gao, R., Xiong, B., Grauman, K.: Im2Flow: motion hallucination from static images for action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5937–5947 (2018)

    Google Scholar 

  11. Girshick, R., Radosavovic, I., Gkioxari, G., Dollár, P., He, K.: Detectron (2018). https://github.com/facebookresearch/detectron

  12. Gkioxari, G., Girshick, R., Dollár, P., He, K.: Detecting and recognizing human-object interactions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8359–8367 (2018)

    Google Scholar 

  13. Gupta, S., Malik, J.: Visual semantic role labeling. arXiv preprint arXiv:1505.04474 (2015)

  14. Gupta, T., Schwing, A., Hoiem, D.: No-frills human-object interaction detection: factorization, appearance and layout encodings, and training techniques. arXiv preprint arXiv:1811.05967 (2018)

  15. Hayes, B., Shah, J.A.: Interpretable models for fast activity recognition and anomaly explanation during collaborative robotics tasks. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 6586–6593. IEEE (2017)

    Google Scholar 

  16. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

    Google Scholar 

  17. Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E.: Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 42, 2011–2023 (2019)

    Article  Google Scholar 

  18. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)

    Google Scholar 

  19. Kolesnikov, A., Kuznetsova, A., Lampert, C., Ferrari, V.: Detecting visual relationships using box attention. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (2019)

    Google Scholar 

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

    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)

    Article  MathSciNet  Google Scholar 

  22. Kuznetsova, A., et al.: The open images dataset v4: unified image classification, object detection, and visual relationship detection at scale. arXiv preprint arXiv:1811.00982 (2018)

  23. Li, Y., Ouyang, W., Zhou, B., Wang, K., Wang, X.: Scene graph generation from objects, phrases and region captions. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1261–1270 (2017)

    Google Scholar 

  24. Li, Y.L., et al.: Transferable interactiveness knowledge for human-object interaction detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3585–3594 (2019)

    Google Scholar 

  25. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  26. Lu, C., Krishna, R., Bernstein, M., Fei-Fei, L.: Visual relationship detection with language priors. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 852–869. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_51

    Chapter  Google Scholar 

  27. Luo, Y., Zheng, Z., Zheng, L., Guan, T., Yu, J., Yang, Y.: Macro-micro adversarial network for human parsing. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 424–440. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01240-3_26

    Chapter  Google Scholar 

  28. Mallya, A., Lazebnik, S.: Learning models for actions and person-object interactions with transfer to question answering. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 414–428. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_25

    Chapter  Google Scholar 

  29. Murphy, K.P., Torralba, A., Freeman, W.T.: Using the forest to see the trees: a graphical model relating features, objects, and scenes. In: Advances in Neural Information Processing Systems, pp. 1499–1506 (2004)

    Google Scholar 

  30. Peyre, J., Laptev, I., Schmid, C., Sivic, J.: Detecting rare visual relations using analogies. arXiv preprint arXiv:1812.05736 (2018)

  31. Qi, S., Wang, W., Jia, B., Shen, J., Zhu, S.-C.: Learning human-object interactions by graph parsing neural networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 407–423. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01240-3_25

    Chapter  Google Scholar 

  32. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)

    Google Scholar 

  33. Shen, L., Yeung, S., Hoffman, J., Mori, G., Fei-Fei, L.: Scaling human-object interaction recognition through zero-shot learning. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1568–1576. IEEE (2018)

    Google Scholar 

  34. Torralba, A., Murphy, K.P., Freeman, W.T., Rubin, M.A.: Context-based vision system for place and object recognition (2003)

    Google Scholar 

  35. Villegas, R., Yang, J., Hong, S., Lin, X., Lee, H.: Decomposing motion and content for natural video sequence prediction. arXiv preprint arXiv:1706.08033 (2017)

  36. Vondrick, C., Pirsiavash, H., Torralba, A.: Generating videos with scene dynamics. In: Advances In Neural Information Processing Systems, pp. 613–621 (2016)

    Google Scholar 

  37. Walker, J., Gupta, A., Hebert, M.: Dense optical flow prediction from a static image. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2443–2451 (2015)

    Google Scholar 

  38. Wan, B., Zhou, D., Liu, Y., Li, R., He, X.: Pose-aware multi-level feature network for human object interaction detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9469–9478 (2019)

    Google Scholar 

  39. Wang, T., et al.: Deep contextual attention for human-object interaction detection. arXiv preprint arXiv:1910.07721 (2019)

  40. Xu, B., Li, J., Wong, Y., Zhao, Q., Kankanhalli, M.S.: Interact as you intend: intention-driven human-object interaction detection. IEEE Trans. Multimed. 22, 1423–1432 (2019)

    Article  Google Scholar 

  41. Xu, B., Wong, Y., Li, J., Zhao, Q., Kankanhalli, M.S.: Learning to detect human-object interactions with knowledge. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)

    Google Scholar 

  42. Xu, D., Zhu, Y., Choy, C.B., Fei-Fei, L.: Scene graph generation by iterative message passing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5410–5419 (2017)

    Google Scholar 

  43. Zhang, H., Kyaw, Z., Chang, S.F., Chua, T.S.: Visual translation embedding network for visual relation detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5532–5540 (2017)

    Google Scholar 

  44. Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(6), 1452–1464 (2017)

    Article  Google Scholar 

  45. Zhou, P., Chi, M.: Relation parsing neural network for human-object interaction detection. In: Proceedings of the IEEE International Conference on Computer Vision (2019)

    Google Scholar 

  46. Zhuang, B., Liu, L., Shen, C., Reid, I.: Towards context-aware interaction recognition for visual relationship detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 589–598 (2017)

    Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the support of the EPSRC Programme Grant Seebibyte EP/M013774/1 and EPSRC Programme Grant CALOPUS EP/R013853/1. The authors would also like to thank Samuel Albanie and Sophia Koepke for helpful suggestions.

Author information

Authors and Affiliations

  1. Visual Geometry Group, University of Oxford, Oxford, UK

    Yang Liu & Andrew Zisserman

  2. Department of Engineering Science, University of Oxford, Oxford, UK

    Qingchao Chen

Authors
  1. Yang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qingchao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrew Zisserman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yang Liu .

Editor information

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

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 17423 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, Y., Chen, Q., Zisserman, A. (2020). Amplifying Key Cues for Human-Object-Interaction Detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12359. Springer, Cham. https://doi.org/10.1007/978-3-030-58568-6_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58568-6_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58567-9

  • Online ISBN: 978-3-030-58568-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation