SpotEM: Efficient Video Search for Episodic Memory

• URL: http://arxiv.org/abs/2306.15850v1
• Date: Wed, 28 Jun 2023 00:52:49 GMT
• Title: SpotEM: Efficient Video Search for Episodic Memory
• Authors: Santhosh Kumar Ramakrishnan, Ziad Al-Halah, Kristen Grauman
• Abstract summary: episodic memory aims to search a long egocentric video to answer a natural language query. Existing methods exhaustively extract expensive fixed-length clip features to look everywhere in the video for the answer. We propose SpotEM, an approach to achieve efficiency for a given EM method while maintaining good accuracy.
• Score: 92.98552727430483
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The goal in episodic memory (EM) is to search a long egocentric video to answer a natural language query (e.g., "where did I leave my purse?"). Existing EM methods exhaustively extract expensive fixed-length clip features to look everywhere in the video for the answer, which is infeasible for long wearable-camera videos that span hours or even days. We propose SpotEM, an approach to achieve efficiency for a given EM method while maintaining good accuracy. SpotEM consists of three key ideas: 1) a novel clip selector that learns to identify promising video regions to search conditioned on the language query; 2) a set of low-cost semantic indexing features that capture the context of rooms, objects, and interactions that suggest where to look; and 3) distillation losses that address the optimization issues arising from end-to-end joint training of the clip selector and EM model. Our experiments on 200+ hours of video from the Ego4D EM Natural Language Queries benchmark and three different EM models demonstrate the effectiveness of our approach: computing only 10% - 25% of the clip features, we preserve 84% - 97% of the original EM model's accuracy. Project page: https://vision.cs.utexas.edu/projects/spotem

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