GPTSee: Enhancing Moment Retrieval and Highlight Detection via Description-Based Similarity Features

• URL: http://arxiv.org/abs/2403.01437v2
• Date: Sun, 10 Mar 2024 09:56:22 GMT
• Title: GPTSee: Enhancing Moment Retrieval and Highlight Detection via Description-Based Similarity Features
• Authors: Yunzhuo Sun, Yifang Xu, Zien Xie, Yukun Shu, and Sidan Du
• Abstract summary: Moment retrieval (MR) and highlight detection (HD) aim to identify relevant moments and highlights in video from natural language query. Existing methods for MR&HD have not yet been integrated with large language models. We propose a novel two-stage model that takes the output of LLMs as the input to the second-stage transformer encoder-decoder.
• Score: 1.614471032380076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Moment retrieval (MR) and highlight detection (HD) aim to identify relevant moments and highlights in video from corresponding natural language query. Large language models (LLMs) have demonstrated proficiency in various computer vision tasks. However, existing methods for MR\&HD have not yet been integrated with LLMs. In this letter, we propose a novel two-stage model that takes the output of LLMs as the input to the second-stage transformer encoder-decoder. First, MiniGPT-4 is employed to generate the detailed description of the video frame and rewrite the query statement, fed into the encoder as new features. Then, semantic similarity is computed between the generated description and the rewritten queries. Finally, continuous high-similarity video frames are converted into span anchors, serving as prior position information for the decoder. Experiments demonstrate that our approach achieves a state-of-the-art result, and by using only span anchors and similarity scores as outputs, positioning accuracy outperforms traditional methods, like Moment-DETR.

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