Zero-shot video highlight detection based on text descriptions and synthetic images

• URL: http://arxiv.org/abs/2609.14790v1
• Date: Sun, 13 Sep 2026 21:03:57 GMT
• Title: Zero-shot video highlight detection based on text descriptions and synthetic images
• Abstract summary: We propose a zero-shot framework that combines CLIP, large language models (LLMs), and diffusion models.<n>LLMs generate textual descriptions of likely highlight events, which are converted into synthetic visual prototypes.<n>Experiments on TVSum and SumMe demonstrate strong zero-shot performance.
• Score: 2.8628939818365935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting video highlights, the most informative or engaging moments in a video, is important for applications such as video summarization and content recommendation. We propose a zero-shot framework that combines CLIP, large language models (LLMs), and diffusion models. Given lightweight video metadata, such as a title or category, an LLM generates textual descriptions of likely highlight events. These descriptions are further converted into synthetic visual prototypes using a diffusion model. Textual and visual representations are matched to video frames using CLIP, enabling frame-level highlight detection without highlight annotations or dataset-specific training. Experiments on TVSum and SumMe demonstrate strong zero-shot performance, with particularly favorable results on TVSum. The proposed approach provides an effective framework for metadata-conditioned zero-shot video highlight detection.

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