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CogVLM: Visual Expert for Pretrained Language Models

Authors :
Wang, Weihan
Lv, Qingsong
Yu, Wenmeng
Hong, Wenyi
Qi, Ji
Wang, Yan
Ji, Junhui
Yang, Zhuoyi
Zhao, Lei
Song, Xixuan
Xu, Jiazheng
Xu, Bin
Li, Juanzi
Dong, Yuxiao
Ding, Ming
Tang, Jie
Publication Year :
2023

Abstract

We introduce CogVLM, a powerful open-source visual language foundation model. Different from the popular shallow alignment method which maps image features into the input space of language model, CogVLM bridges the gap between the frozen pretrained language model and image encoder by a trainable visual expert module in the attention and FFN layers. As a result, CogVLM enables deep fusion of vision language features without sacrificing any performance on NLP tasks. CogVLM-17B achieves state-of-the-art performance on 10 classic cross-modal benchmarks, including NoCaps, Flicker30k captioning, RefCOCO, RefCOCO+, RefCOCOg, Visual7W, GQA, ScienceQA, VizWiz VQA and TDIUC, and ranks the 2nd on VQAv2, OKVQA, TextVQA, COCO captioning, etc., surpassing or matching PaLI-X 55B. Codes and checkpoints are available at https://github.com/THUDM/CogVLM.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2311.03079
Document Type :
Working Paper