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C2C: Component-to-Composition Learning for Zero-Shot Compositional Action Recognition

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

Compositional actions consist of dynamic (verbs) and static (objects) concepts. Humans can easily recognize unseen compositions using the learned concepts. For machines, solving such a problem requires a model to recognize unseen actions composed of previously observed verbs and objects, thus requiring so-called compositional generalization ability. To facilitate this research, we propose a novel Zero-Shot Compositional Action Recognition (ZS-CAR) task. For evaluating the task, we construct a new benchmark, Something-composition (Sth-com), based on the widely used Something-Something V2 dataset. We also propose a novel Component-to-Composition (C2C) learning method to solve the new ZS-CAR task. C2C includes an independent component learning module and a composition inference module. Last, we devise an enhanced training strategy to address the challenges of component variations between seen and unseen compositions and to handle the subtle balance between learning seen and unseen actions. The experimental results demonstrate that the proposed framework significantly surpasses the existing compositional generalization methods and sets a new state-of-the-art. The new Sth-com benchmark and code are available at https://github.com/RongchangLi/ZSCAR_C2C.

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References

  1. Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lučić, M., Schmid, C.: Vivit: a video vision transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6836–6846 (2021)

    Google Scholar 

  2. Bertasius, G., Wang, H., Torresani, L.: Is space-time attention all you need for video understanding? In: International Conference on Machine Learning, pp. 813–824 (2021)

    Google Scholar 

  3. Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)

    Article  Google Scholar 

  4. Brattoli, B., Tighe, J., Zhdanov, F., Perona, P., Chalupka, K.: Rethinking zero-shot video classification: end-to-end training for realistic applications. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4613–4623 (2020)

    Google Scholar 

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

    Google Scholar 

  6. Chao, W.L., Changpinyo, S., Gong, B., Sha, F.: An empirical study and analysis of generalized zero-shot learning for object recognition in the wild. In: Proceedings of the European Conference on Computer Vision, pp. 52–68 (2016)

    Google Scholar 

  7. Chen, S., Huang, D.: Elaborative rehearsal for zero-shot action recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 13638–13647 (2021)

    Google Scholar 

  8. Chomsky, N.: Aspects of the Theory of Syntax, no. 11. MIT press, Cambridge (2014)

    Google Scholar 

  9. Doshi, K., Yilmaz, Y.: Zero-shot action recognition with transformer-based video semantic embedding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4858–4867 (2023)

    Google Scholar 

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

    Google Scholar 

  11. Fan, H., et al.: Multiscale vision transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6824–6835 (2021)

    Google Scholar 

  12. Feichtenhofer, C.: X3d: expanding architectures for efficient video recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 203–213 (2020)

    Google Scholar 

  13. Feichtenhofer, C., Fan, H., Malik, J., He, K.: Slowfast networks for video recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6202–6211 (2019)

    Google Scholar 

  14. Fodor, J.A., Pylyshyn, Z.W.: Connectionism and cognitive architecture: a critical analysis. Cognition 28(1–2), 3–71 (1988)

    Article  Google Scholar 

  15. Fu, Y., Hospedales, T.M., Xiang, T., Gong, S.: Attribute learning for understanding unstructured social activity. In: Proceedings of the European Conference on Computer Vision, pp. 530–543 (2012)

    Google Scholar 

  16. Goyal, R., et al.: The “something something" video database for learning and evaluating visual common sense. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5842–5850 (2017)

    Google Scholar 

  17. Gretton, A., Bousquet, O., Smola, A., Schölkopf, B.: Measuring statistical dependence with hilbert-schmidt norms. In: International Conference on Algorithmic Learning Theory, pp. 63–77 (2005)

    Google Scholar 

  18. Hao, S., Han, K., Wong, K.Y.K.: Learning attention as disentangler for compositional zero-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15315–15324 (2023)

    Google Scholar 

  19. Hara, K., Kataoka, H., Satoh, Y.: Can spatiotemporal 3D cnns retrace the history of 2d cnns and imagenet? In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6546–6555 (2018)

    Google Scholar 

  20. Heckman, J.J.: Sample selection bias as a specification error. Econometrica: J. Econometric Soc. 153–161 (1979)

    Google Scholar 

  21. Isola, P., Lim, J.J., Adelson, E.H.: Discovering states and transformations in image collections. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1383–1391 (2015)

    Google Scholar 

  22. Ji, J., Krishna, R., Fei-Fei, L., Niebles, J.C.: Action genome: actions as compositions of spatio-temporal scene graphs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10236–10247 (2020)

    Google Scholar 

  23. Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221–231 (2012)

    Article  Google Scholar 

  24. Karthik, S., Mancini, M., Akata, Z.: Kg-sp: knowledge guided simple primitives for open world compositional zero-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9336–9345 (2022)

    Google Scholar 

  25. Kay, W., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017)

  26. Kim, H., Lee, J., Park, S., Sohn, K.: Hierarchical visual primitive experts for compositional zero-shot learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5675–5685 (2023)

    Google Scholar 

  27. Kwon, H., Kim, M., Kwak, S., Cho, M.: Motionsqueeze: neural motion feature learning for video understanding. In: Proceedings of the European Conference on Computer Vision, pp. 345–362 (2020)

    Google Scholar 

  28. Kwon, H., Kim, M., Kwak, S., Cho, M.: Learning self-similarity in space and time as generalized motion for video action recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 13065–13075 (2021)

    Google Scholar 

  29. Li, K., et al.: Uniformer: unified transformer for efficient spatiotemporal representation learning. arXiv preprint arXiv:2201.04676 (2022)

  30. Li, R.C., Wu, X.J., Wu, C., Xu, T.Y., Kittler, J.: Dynamic information enhancement for video classification. Image Vis. Comput. 114, 104244 (2021)

    Article  Google Scholar 

  31. Li, R., Wu, X.J., Xu, T.: Video is graph: structured graph module for video action recognition. arXiv preprint arXiv:2110.05904 (2021)

  32. Li, R., Xu, T., Wu, X.J., Shen, Z., Kittler, J.: Perceiving actions via temporal video frame pairs. ACM Trans. Intell. Syst. Technol. 15(3), 1–20 (2024)

    Article  Google Scholar 

  33. Li, X., Yang, X., Wei, K., Deng, C., Yang, M.: Siamese contrastive embedding network for compositional zero-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9326–9335 (2022)

    Google Scholar 

  34. Li, Y., Ji, B., Shi, X., Zhang, J., Kang, B., Wang, L.: Tea: temporal excitation and aggregation for action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 909–918 (2020)

    Google Scholar 

  35. Lin, J., Gan, C., Han, S.: TSM: temporal shift module for efficient video understanding. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7083–7093 (2019)

    Google Scholar 

  36. Liu, J., Kuipers, B., Savarese, S.: Recognizing human actions by attributes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3337–3344 (2011)

    Google Scholar 

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

    Google Scholar 

  38. Lu, X., Guo, S., Liu, Z., Guo, J.: Decomposed soft prompt guided fusion enhancing for compositional zero-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 23560–23569 (2023)

    Google Scholar 

  39. Ma, L., Zheng, Y., Zhang, Z., Yao, Y., Fan, X., Ye, Q.: Motion stimulation for compositional action recognition. IEEE Trans. Circuits Syst. Video Technol. 33, 2061–2074 (2022)

    Article  Google Scholar 

  40. Mancini, M., Naeem, M.F., Xian, Y., Akata, Z.: Open world compositional zero-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5222–5230 (2021)

    Google Scholar 

  41. Mandal, D., et al.: Out-of-distribution detection for generalized zero-shot action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9985–9993 (2019)

    Google Scholar 

  42. Materzynska, J., Xiao, T., Herzig, R., Xu, H., Wang, X., Darrell, T.: Something-else: compositional action recognition with spatial-temporal interaction networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1049–1059 (2020)

    Google Scholar 

  43. Misra, I., Gupta, A., Hebert, M.: From red wine to red tomato: composition with context. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1792–1801 (2017)

    Google Scholar 

  44. Naeem, M.F., Xian, Y., Tombari, F., Akata, Z.: Learning graph embeddings for compositional zero-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 953–962 (2021)

    Google Scholar 

  45. Nayak, N.V., Yu, P., Bach, S.: Learning to compose soft prompts for compositional zero-shot learning. In: The Eleventh International Conference on Learning Representations (2022)

    Google Scholar 

  46. Neimark, D., Bar, O., Zohar, M., Asselmann, D.: Video transformer network. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3163–3172 (2021)

    Google Scholar 

  47. Pan, J., Lin, Z., Zhu, X., Shao, J., Li, H.: St-adapter: parameter-efficient image-to-video transfer learning. Adv. Neural. Inf. Process. Syst. 35, 26462–26477 (2022)

    Google Scholar 

  48. Park, J., Lee, J., Sohn, K.: Dual-path adaptation from image to video transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2203–2213 (2023)

    Google Scholar 

  49. Piergiovanni, A., Kuo, W., Angelova, A.: Rethinking video vits: sparse video tubes for joint image and video learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2214–2224 (2023)

    Google Scholar 

  50. Purushwalkam, S., Nickel, M., Gupta, A., Ranzato, M.: Task-driven modular networks for zero-shot compositional learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3593–3602 (2019)

    Google Scholar 

  51. Qian, R., Lin, W., See, J., Li, D.: Controllable augmentations for video representation learning. Visual Intell. 2(1), 1 (2024)

    Article  Google Scholar 

  52. Qian, Y., Yu, L., Liu, W., Hauptmann, A.G.: Rethinking zero-shot action recognition: learning from latent atomic actions. In: Proceedings of the European Conference on Computer Vision, pp. 104–120 (2022)

    Google Scholar 

  53. Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748–8763. PMLR (2021)

    Google Scholar 

  54. Ruis, F., Burghouts, G., Bucur, D.: Independent prototype propagation for zero-shot compositionality. Adv. Neural. Inf. Process. Syst. 34, 10641–10653 (2021)

    Google Scholar 

  55. Saini, N., Pham, K., Shrivastava, A.: Disentangling visual embeddings for attributes and objects. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13658–13667 (2022)

    Google Scholar 

  56. Sun, P., Wu, B., Li, X., Li, W., Duan, L., Gan, C.: Counterfactual debiasing inference for compositional action recognition. In: Proceedings of the ACM International Conference on Multimedia, pp. 3220–3228 (2021)

    Google Scholar 

  57. Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4489–4497 (2015)

    Google Scholar 

  58. Wang, L., Tong, Z., Ji, B., Wu, G.: TDN: temporal difference networks for efficient action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1895–1904 (2021)

    Google Scholar 

  59. Wang, L., et al.: Temporal segment networks: towards good practices for deep action recognition. In: Proceedings of the European Conference on Computer Vision, pp. 20–36 (2016)

    Google Scholar 

  60. Wang, Q., et al.: Learning conditional attributes for compositional zero-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11197–11206 (2023)

    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: Proceedings of the European Conference on Computer Vision, pp. 305–321 (2018)

    Google Scholar 

  62. Xu, T., Zhu, X.F., Wu, X.J.: Learning spatio-temporal discriminative model for affine subspace based visual object tracking. Visual Intell. 1(1), 4 (2023)

    Article  Google Scholar 

  63. Xu, X., Hospedales, T., Gong, S.: Transductive zero-shot action recognition by word-vector embedding. Int. J. Comput. Vision 123, 309–333 (2017)

    Article  MathSciNet  Google Scholar 

  64. Yan, R., Huang, P., Shu, X., Zhang, J., Pan, Y., Tang, J.: Look less think more: rethinking compositional action recognition. In: Proceedings of the ACM International Conference on Multimedia, pp. 3666–3675 (2022)

    Google Scholar 

  65. Yan, R., Xie, L., Shu, X., Zhang, L., Tang, J.: Progressive instance-aware feature learning for compositional action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 45, 10317–103330 (2023)

    Article  Google Scholar 

  66. Yang, T., Zhu, Y., Xie, Y., Zhang, A., Chen, C., Li, M.: Aim: adapting image models for efficient video action recognition. arXiv preprint arXiv:2302.03024 (2023)

  67. Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6023–6032 (2019)

    Google Scholar 

  68. Zadrozny, B.: Learning and evaluating classifiers under sample selection bias. In: Proceedings of the International Conference on Machine Learning, p. 114 (2004)

    Google Scholar 

  69. Zellers, R., Choi, Y.: Zero-shot activity recognition with verb attribute induction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 946–958 (2017)

    Google Scholar 

  70. Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Learning to prompt for vision-language models. Int. J. Comput. Vision 130(9), 2337–2348 (2022)

    Article  Google Scholar 

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Acknowledgements

This work is supported in part by the National Key Research and Development Program of China (2023YFF1105102, 2023YFF1105105), the National Natural Science Foundation of China (62020106012, 62332008, 62106089, U1836218, 62336004), the 111 Project of Ministry of Education of China (B12018), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX22_2307), and the UK EPSRC (EP/V002856/1, EP/T022205/1).

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Authors and Affiliations

  1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China

    Rongchang Li, Zhenhua Feng, Tianyang Xu, Linze Li & Xiao-Jun Wu

  2. Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, Guildford, UK

    Rongchang Li, Zhenhua Feng, Muhammad Awais, Sara Atito & Josef Kittler

  3. School of Computer Science and Electronic Engineering, University of Surrey, Guildford, UK

    Zhenhua Feng & Josef Kittler

  4. Surrey Institute of People-centred AI (SI-PAI), University of Surrey, Guildford, UK

    Zhenhua Feng, Muhammad Awais & Sara Atito

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  1. Rongchang Li
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  3. Tianyang Xu
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Correspondence to Xiao-Jun Wu .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Hessen, Germany

    Stefan Roth

  4. Princeton University, Palo Alto, CA, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Li, R. et al. (2025). C2C: Component-to-Composition Learning for Zero-Shot Compositional Action Recognition. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15096. Springer, Cham. https://doi.org/10.1007/978-3-031-72920-1_21

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