FILS: Self-Supervised Video Feature Prediction In Semantic Language Space

• URL: http://arxiv.org/abs/2406.03447v1
• Date: Wed, 5 Jun 2024 16:44:06 GMT
• Title: FILS: Self-Supervised Video Feature Prediction In Semantic Language Space
• Authors: Mona Ahmadian, Frank Guerin, Andrew Gilbert,
• Abstract summary: This paper demonstrates a self-supervised approach for learning semantic video representations. We present FILS, a novel self-supervised video Feature prediction In semantic Language Space.
• Score: 11.641926922266347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper demonstrates a self-supervised approach for learning semantic video representations. Recent vision studies show that a masking strategy for vision and natural language supervision has contributed to developing transferable visual pretraining. Our goal is to achieve a more semantic video representation by leveraging the text related to the video content during the pretraining in a fully self-supervised manner. To this end, we present FILS, a novel self-supervised video Feature prediction In semantic Language Space (FILS). The vision model can capture valuable structured information by correctly predicting masked feature semantics in language space. It is learned using a patch-wise video-text contrastive strategy, in which the text representations act as prototypes for transforming vision features into a language space, which are then used as targets for semantically meaningful feature prediction using our masked encoder-decoder structure. FILS demonstrates remarkable transferability on downstream action recognition tasks, achieving state-of-the-art on challenging egocentric datasets, like Epic-Kitchens, Something-SomethingV2, Charades-Ego, and EGTEA, using ViT-Base. Our efficient method requires less computation and smaller batches compared to previous works.

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