The content introduces TraveLER, a novel framework for video question answering (VideoQA) that utilizes a multi-agent approach. The key components of the framework are:
The framework is designed to address limitations of existing approaches that use image-based models for VideoQA. These methods often overlook how keyframes are selected and cannot adjust when incorrect timestamps are identified. Moreover, they provide general descriptions of frames rather than extracting details relevant to the question.
In contrast, TraveLER iteratively collects information, allowing it to adaptively focus on relevant parts of the video and extract fine-grained details to answer the question. Extensive experiments show that TraveLER outperforms state-of-the-art zero-shot VideoQA methods on benchmarks like NExT-QA, STAR, and Perception Test.
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arxiv.org
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by Chuyi Shang,... at arxiv.org 04-03-2024
https://arxiv.org/pdf/2404.01476.pdfDeeper Inquiries