Multimodal and large language models (LLMs) have revolutionized the utilization of open-world knowledge, unlocking novel potentials across various tasks and applications. Among these domains, the video domain has notably benefited from their capabilities. In this paper, we present Highlight-CLIP (HL-CLIP), a method designed to excel in the video highlight detection task by leveraging the pre-trained knowledge embedded in multimodal models. By simply fine-tuning the multimodal encoder in combination with our innovative saliency pooling technique, we have achieved the state-of-the-art performance in the highlight detection task, the QVHighlight Benchmark, to the best of our knowledge.
@article{arxiv.2404.01745,
title = {Unleash the Potential of CLIP for Video Highlight Detection},
author = {Donghoon Han and Seunghyeon Seo and Eunhwan Park and Seong-Uk Nam and Nojun Kwak},
journal= {arXiv preprint arXiv:2404.01745},
year = {2024}
}