VLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and
  Visual Documents
Authors: name: Rui Meng, name: Ziyan Jiang, name: Ye Liu, name: Mingyi Su, name: Xinyi Yang, name: Yuepeng Fu, name: Can Qin, name: Zeyuan Chen, name: Ran Xu, name: Caiming Xiong, name: Yingbo Zhou, name: Wenhu Chen, name: Semih Yavuz

Multimodal embedding models have been crucial in enabling various downstream
tasks such as semantic similarity, information retrieval, and clustering over
different modalities. However, existing multimodal embeddings like VLM2Vec,
E5-V, GME are predominantly focused on natural images, with limited support for
other visual forms such as videos and visual documents. This restricts their
applicability in real-world scenarios, including AI agents, multi-modal search
and recommendation, and retrieval-augmented generation (RAG). To close this
gap, we propose VLM2Vec-V2, a unified framework for learning embeddings across
diverse visual forms. First, we introduce MMEB-V2, a comprehensive benchmark
that extends MMEB with five new task types: visual document retrieval, video
retrieval, temporal grounding, video classification and video question
answering - spanning text, image, video, and visual document inputs. Next, we
train VLM2Vec-V2, a general-purpose embedding model that supports text, image,
video, and visual document inputs. Extensive experiments show that VLM2Vec-V2
achieves strong performance not only on the newly introduced video and document
retrieval tasks, but also improves over prior baselines on the original image
benchmarks. Through extensive evaluation, our study offers insights into the
generalizability of various multimodal embedding models and highlights
effective strategies for unified embedding learning, laying the groundwork for
more scalable and adaptable representation learning in both research and
real-world settings.