TA2N: Two-Stage Action Alignment Network for Few-Shot Action Recognition


  • Shuyuan Li Shanghai Jiao Tong University
  • Huabin Liu Shanghai Jiao Tong University
  • Rui Qian Shanghai Jiao Tong University
  • Yuxi Li Shanghai Jiao Tong University
  • John See Heriot-Watt University Malaysia
  • Mengjuan Fei Huawei Cloud
  • Xiaoyuan Yu Huawei Cloud
  • Weiyao Lin Shanghai Jiao Tong University




Computer Vision (CV)


Few-shot action recognition aims to recognize novel action classes (query) using just a few samples (support). The majority of current approaches follow the metric learning paradigm, which learns to compare the similarity between videos. Recently, it has been observed that directly measuring this similarity is not ideal since different action instances may show distinctive temporal distribution, resulting in severe misalignment issues across query and support videos. In this paper, we arrest this problem from two distinct aspects -- action duration misalignment and action evolution misalignment. We address them sequentially through a Two-stage Action Alignment Network (TA2N). The first stage locates the action by learning a temporal affine transform, which warps each video feature to its action duration while dismissing the action-irrelevant feature (e.g. background). Next, the second stage coordinates query feature to match the spatial-temporal action evolution of support by performing temporally rearrange and spatially offset prediction. Extensive experiments on benchmark datasets show the potential of the proposed method in achieving state-of-the-art performance for few-shot action recognition.




How to Cite

Li, S., Liu, H., Qian, R., Li, Y., See, J., Fei, M., Yu, X., & Lin, W. (2022). TA2N: Two-Stage Action Alignment Network for Few-Shot Action Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 36(2), 1404-1411. https://doi.org/10.1609/aaai.v36i2.20029



AAAI Technical Track on Computer Vision II