VidVec: Unlocking Video MLLM Embeddings for Video-Text Retrieval

Abstract

Recent studies have adapted generative Multimodal Large Language Models (MLLMs) into embedding extractors for text-vision tasks, typically by fine-tuning them to produce universal representations. However, their performance on video-text retrieval often remains below that of dedicated Video Foundation Models. In this paper, we revisit the video-text retrieval capabilities of MLLMs. We first analyze the zero-shot retrieval capabilities of pretrained MLLMs and show that they already encode substantial retrieval-relevant information: combining intermediate-layer embeddings with a calibrated MLLM head yields strong performance without any training. To further exploit these capabilities, we introduce a lightweight text-based optimization strategy that requires no paired multimodal data. Our method uses dense video captions as rich textual surrogates for video semantics and short summaries as compact, query-like views, showing that a carefully designed text-alignment objective can improve multimodal alignment through the shared MLLM backbone. We backup these findings through in-depth analysis. Remarkably, our lightweight text-only optimization outperforms its counterpart trained with paired video-text supervision, which is substantially more computationally expensive. Furthermore, our method outperforms existing approaches, often by a substantial margin, and achieves state-of-the-art performance across common video-text retrieval benchmarks. More broadly, our findings highlight the untapped retrieval capabilities of pretrained MLLMs and establish text-only optimization as a more efficient and effective substitute for large-scale visual supervision.