COMEDIAN: Self-Supervised Learning and Knowledge Distillation for Action Spotting using Transformers

Abstract

We present COMEDIAN, a novel pipeline to initialize spatio-temporal transformers for action spotting, which involves self-supervised learning and knowledge distillation. Action spotting is a timestamp-level temporal action detection task. Our pipeline consists of three steps, with two initialization stages. First, we perform self-supervised initialization of a spatial transformer using short videos as input. Additionally, we initialize a temporal transformer that enhances the spatial transformer’s outputs with global context through knowledge distillation from a pre-computed feature bank aligned with each short video segment. In the final step, we fine-tune the transformers to the action spotting task. The experiments, conducted on the SoccerNet-v2 dataset, demonstrate state-of-the-art performance and validate the effectiveness of COMEDIAN’s pretraining paradigm. Our results highlight several advantages of our pretraining pipeline, including improved performance and faster convergence compared to non-pretrained models.

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Citation

If you found our work useful, please consider citing us:

@InProceedings{Denize_2024_WACVW,
   author    = {Denize, Julien and Liashuha, Mykola and Rabarisoa, Jaonary and Orcesi, Astrid and H\'erault, Romain},
   title     = {COMEDIAN: Self-Supervised Learning and Knowledge Distillation for Action Spotting Using Transformers},
   booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops},
   month     = {January},
   year      = {2024},
   pages     = {530-540}
}