VISTA: Visualized Text Embedding For Universal Multi-Modal Retrieval

Autor:	Zhou, Junjie, Liu, Zheng, Xiao, Shitao, Zhao, Bo, Xiong, Yongping
Rok vydání:	2024
Předmět:	Computer Science - Information Retrieval Computer Science - Computation and Language Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Multi-modal retrieval becomes increasingly popular in practice. However, the existing retrievers are mostly text-oriented, which lack the capability to process visual information. Despite the presence of vision-language models like CLIP, the current methods are severely limited in representing the text-only and image-only data. In this work, we present a new embedding model VISTA for universal multi-modal retrieval. Our work brings forth threefold technical contributions. Firstly, we introduce a flexible architecture which extends a powerful text encoder with the image understanding capability by introducing visual token embeddings. Secondly, we develop two data generation strategies, which bring high-quality composed image-text to facilitate the training of the embedding model. Thirdly, we introduce a multi-stage training algorithm, which first aligns the visual token embedding with the text encoder using massive weakly labeled data, and then develops multi-modal representation capability using the generated composed image-text data. In our experiments, VISTA achieves superior performances across a variety of multi-modal retrieval tasks in both zero-shot and supervised settings. Our model, data, and source code are available at https://github.com/FlagOpen/FlagEmbedding. Comment: Accepted to ACL 2024 main conference
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.04292 Zobrazit plný text záznamu View this record from Arxiv