CogVLM: Visual Expert for Pretrained Language Models
Authors: name: Weihan Wang, name: Qingsong Lv, name: Wenmeng Yu, name: Wenyi Hong, name: Ji Qi, name: Yan Wang, name: Junhui Ji, name: Zhuoyi Yang, name: Lei Zhao, name: Xixuan Song, name: Jiazheng Xu, name: Bin Xu, name: Juanzi Li, name: Yuxiao Dong, name: Ming Ding, name: Jie Tang

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.