Summary of Gptsee: Enhancing Moment Retrieval and Highlight Detection Via Description-based Similarity Features, by Yunzhuo Sun et al.
GPTSee: Enhancing Moment Retrieval and Highlight Detection via Description-Based Similarity Features
by Yunzhuo Sun, Yifang Xu, Zien Xie, Yukun Shu, Sidan Du
First submitted to arxiv on: 3 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel two-stage model for moment retrieval (MR) and highlight detection (HD) in videos. The approach integrates large language models (LLMs) with transformer encoder-decoders to identify relevant moments and highlights from natural language queries. The first stage uses MiniGPT-4 to generate detailed descriptions of video frames and rewritten query statements, which are then fed into the second stage as new features. Semantic similarity is computed between these generated descriptions and rewritten queries, allowing for continuous high-similarity video frames to be converted into span anchors, serving as prior position information for the decoder. The proposed approach achieves state-of-the-art results in MR&HD tasks, outperforming traditional methods like Moment-DETR. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to find important moments and highlights in videos by using computer programs. Right now, these programs are not very good at doing this job on their own. The researchers came up with a new way to make the programs work better. They used big language models that are good at understanding text and combining it with pictures. This new method is called moment retrieval and highlight detection (MR&HD). It helps computers find the most important parts of a video by comparing what’s happening in the video with what someone says about it. The results show that this new way is better than older methods, making it easier to automatically understand videos. |
Keywords
» Artificial intelligence » Decoder » Transformer
