Contrastive Auxiliary Training in Video-JEPA for Few-Shot Action Recognition on EPIC-Kitchens-100

Abstract

Abstract: With the recent surge in the research of vision transformers, they have demonstrated remarkable potential for various challenging computer vision applications, such as image recognition, point cloud classification as well as video understanding. In this paper, we present empirical results for training a stronger video vision transformer on the EPIC-KITCHENS-100 Action Recognition dataset. Specifically, we explore training techniques for video vision transformers, such as augmentations, resolutions as well as initialization, etc. With our training recipe, a single ViViT model achieves the perfo

Research Question

How does contrastive auxiliary training in Video-JEPA affect few-shot action recognition accuracy on EPIC-Kitchens-100 compared to reconstructive objectives?

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