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

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@article{denize_2023_COMEDIAN,
   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},
   journal   = {arXiv},
   volume    = {abs/2309.01270},
   year      = {2023},
   url       = {https://arxiv.org/abs/2309.01270},
}