  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.
