Summary of Vlm2vec: Training Vision-language Models For Massive Multimodal Embedding Tasks, by Ziyan Jiang et al.
VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks
by Ziyan Jiang, Rui Meng, Xinyi Yang, Semih Yavuz, Yingbo Zhou, Wenhu Chen
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A universal text embedding model is crucial for various downstream tasks such as semantic similarity, information retrieval, and clustering. Recent progress has been slow despite its importance and practicality. This paper aims to explore building universal embeddings capable of handling a wide range of tasks. The contributions are twofold: the Massive Multimodal Embedding Benchmark (MMEB) covering 4 meta-tasks and 36 datasets, including training and evaluation sets; and VLM2Vec, a contrastive training framework that converts state-of-the-art vision-language models into embedding models via MMEB. Unlike previous models like CLIP and BLIP, VLM2Vec can process any combination of images and text to generate a fixed-dimensional vector based on task instructions. The results show an absolute average improvement of 10% to 20% over existing multimodal embedding models on both in-distribution and out-of-distribution datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Universal text embeddings are important for various tasks. This paper tries to make progress by building universal embeddings that can handle many different tasks. They create a big benchmark with many different datasets, and then use a special training framework called VLM2Vec to convert state-of-the-art vision-language models into embedding models. This is different from other models that only work on text or images separately. The results show that their method works better than others. |
Keywords
» Artificial intelligence » Clustering » Embedding
