VLM2Vec: Training Vision-Language Models for Massive Multimodal
  Embedding Tasks
Authors: name: Ziyan Jiang, name: Rui Meng, name: Xinyi Yang, name: Semih Yavuz, name: Yingbo Zhou, name: Wenhu Chen

Embedding models have been crucial in enabling various downstream tasks such
as semantic similarity, information retrieval, and clustering. Recently, there
has been a surge of interest in developing universal text embedding models that
can generalize across tasks (e.g., MTEB). However, progress in learning
universal multimodal embedding models has been relatively slow despite their
importance. In this work, we aim to explore the potential for building
universal embeddings capable of handling a wide range of downstream tasks. Our
contributions are twofold: (1) MMEB (Massive Multimodal Embedding Benchmark),
which covers 4 meta-tasks (i.e. classification, visual question answering,
multimodal retrieval, and visual grounding) and 36 datasets, including 20
training and 16 evaluation datasets, and (2) VLM2Vec (Vision-Language Model ->
Vector), a contrastive training framework that converts any state-of-the-art
vision-language model into an embedding model via training on MMEB. Unlike
previous models such as CLIP and BLIP, VLM2Vec can process any combination of
images and text to generate a fixed-dimensional vector based on task
instructions. We build a series of VLM2Vec models on Phi-3.5-V and evaluate
them on MMEB's evaluation split. Our results show that \model achieves an
absolute average improvement of 10% to 20% over existing multimodal embedding
models on both in-distribution and out-of-distribution datasets in MMEB.