VidVec: Unlocking Video MLLM Embeddings for Video-Text Retrieval

• URL: http://arxiv.org/abs/2602.08099v1
• Date: Sun, 08 Feb 2026 19:39:32 GMT
• Title: VidVec: Unlocking Video MLLM Embeddings for Video-Text Retrieval
• Authors: Issar Tzachor, Dvir Samuel, Rami Ben-Ari,
• Abstract summary: This paper focuses on leveraging MLLMs for video-text embedding and retrieval.<n>We first conduct a systematic layer-wise analysis, showing that intermediate (pre-trained) MLLM layers already encode substantial task-relevant information.<n>We demonstrate that combining intermediate-layer embeddings with a calibrated MLLM head yields strong zero-shot retrieval performance without any training.
• Score: 11.519642157641023
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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