VidVec: Unlocking Video MLLM Embeddings for Video-Text Retrieval

issart, Dvir Samuel, Rami Ben-Ari

VidVec: Unlocking Video MLLM Embeddings for Video-Text Retrieval: 23 upvotes on Hugging Face Daily Papers, #2 of 48 papers on 2026-02-12. Day-by-day upvote history. It lost 102 votes when the Hub removed votes in bulk.

Recent studies have adapted generative Multimodal Large Language Models (MLLMs) into embedding extractors for vision tasks, typically through fine-tuning to produce universal representations. However, their performance on video remains inferior to Video Foundation Models (VFMs). In this paper, we focus on leveraging MLLMs for video-text embedding and retrieval. We first conduct a systematic layer-wise analysis, showing that intermediate (pre-trained) MLLM layers already encode substantial task-relevant information. Leveraging this insight, we demonstrate that combining intermediate-layer embeddings with a calibrated MLLM head yields strong zero-shot retrieval performance without any training. Building on these findings, we introduce a lightweight text-based alignment strategy which maps dense video captions to short summaries and enables task-related video-text embedding learning without visual supervision. Remarkably, without any fine-tuning beyond text, our method outperforms current methods, often by a substantial margin, achieving state-of-the-art results across common video retrieval benchmarks.

Paper page on Hugging Face · arXiv

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