Summary of Unleash the Potential Of Clip For Video Highlight Detection, by Donghoon Han et al.
Unleash the Potential of CLIP for Video Highlight Detection
by Donghoon Han, Seunghyeon Seo, Eunhwan Park, Seong-Uk Nam, Nojun Kwak
First submitted to arxiv on: 2 Apr 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 presents Highlight-CLIP (HL-CLIP), a method that leverages pre-trained multimodal models for video highlight detection. The authors fine-tune the multimodal encoder and combine it with an innovative saliency pooling technique to achieve state-of-the-art performance on the QVHighlight Benchmark, which is the best known benchmark for this task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video highlight detection has benefited significantly from large language models (LLMs) and multimodal models. The authors introduce HL-CLIP, a method that excels in this task by fine-tuning the multimodal encoder and combining it with a novel saliency pooling technique. This approach achieves state-of-the-art performance on the QVHighlight Benchmark. |
Keywords
» Artificial intelligence » Encoder » Fine tuning
