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arXiv · 2601.13797

PREGEN: Uncovering Latent Thoughts in Composed Video Retrieval

Gabriele Serussi·David Vainshtein·Jonathan Kouchly·Dotan Di Castro·Chaim Baskin
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Abstract

Composed Video Retrieval (CoVR) aims to retrieve a video based on a query video and a modifying text. Current CoVR methods fail to fully exploit modern Vision-Language Models (VLMs), either using outdated architectures or requiring computationally expensive fine-tuning and slow caption generation. We introduce PREGEN (PRE GENeration extraction), an efficient and powerful CoVR framework that overcomes these limitations. Our approach uniquely pairs a frozen, pre-trained VLM with a lightweight encoding model, eliminating the need for any VLM fine-tuning. We feed the query video and modifying text into the VLM and extract the hidden state of the final token from each layer. A simple encoder is then trained on these pooled representations, creating a semantically rich and compact embedding for retrieval. PREGEN significantly advances the state of the art, surpassing all prior methods on standard CoVR benchmarks with substantial gains in Recall@1 of +27.23 and +69.59. Our method demonstrates robustness across different VLM backbones and exhibits strong zero-shot generalization to more complex textual modifications, highlighting its effectiveness and semantic capabilities.

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Gabriele Serussi, David Vainshtein, Jonathan Kouchly, Dotan Di Castro, Chaim Baskin. 2026-01-20. PREGEN: Uncovering Latent Thoughts in Composed Video Retrieval. https://arxiv.org/abs/2601.13797

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