CVPR 2021 Paper

Toby Perrett, Alessandro Masullo, Tilo Burghardt, Majid Mirmehdi and Dima Damen


We propose a novel approach to few-shot action recognition, finding temporally-corresponding frame tuples between the query and videos in the support set. Distinct from previous few-shot works, we construct class prototypes using the CrossTransformer attention mechanism to observe relevant sub-sequences of all support videos, rather than using class averages or single best matches. Video representations are formed from ordered tuples of varying numbers of frames, which allows sub-sequences of actions at different speeds and temporal offsets to be compared.

Our proposed Temporal-Relational CrossTransformers (TRX) achieve state-of-the-art results on few-shot splits of Kinetics, Something-Something V2 (SSv2), HMDB51 and UCF101. Importantly, our method outperforms prior work on SSv2 by a wide margin (12%) due to the its ability to model temporal relations. A detailed ablation showcases the importance of matching to multiple support set videos and learning higher-order relational CrossTransformers.






    author    = {Perrett, Toby and Masullo, Alessandro and Burghardt, Tilo and Mirmehdi, Majid and Damen, Dima},
    title     = {Temporal-Relational CrossTransformers for Few-Shot Action Recognition},
    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year      = {2021}


This research is supported by EPSRC SPHERE Next Steps (EP/R005273/1). Damen is supported by EPSRC Fellowship UMPIRE~(EP/T004991/1).