Text-Conditioned Resampler For Long Form Video Understanding

• URL: http://arxiv.org/abs/2312.11897v3
• Date: Mon, 19 Aug 2024 12:47:11 GMT
• Title: Text-Conditioned Resampler For Long Form Video Understanding
• Authors: Bruno Korbar, Yongqin Xian, Alessio Tonioni, Andrew Zisserman, Federico Tombari,
• Abstract summary: We present a text-conditioned video resampler (TCR) module that uses a pre-trained visual encoder and large language model (LLM) TCR can process more than 100 frames at a time with plain attention and without optimised implementations.
• Score: 94.81955667020867
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper we present a text-conditioned video resampler (TCR) module that uses a pre-trained and frozen visual encoder and large language model (LLM) to process long video sequences for a task. TCR localises relevant visual features from the video given a text condition and provides them to a LLM to generate a text response. Due to its lightweight design and use of cross-attention, TCR can process more than 100 frames at a time with plain attention and without optimised implementations. We make the following contributions: (i) we design a transformer-based sampling architecture that can process long videos conditioned on a task, together with a training method that enables it to bridge pre-trained visual and language models; (ii) we identify tasks that could benefit from longer video perception; and (iii) we empirically validate its efficacy on a wide variety of evaluation tasks including NextQA, EgoSchema, and the EGO4D-LTA challenge.

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