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CogVLM: Visual Expert for Pretrained Language Models

About

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.

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

Related benchmarks

TaskDatasetResultRank
Visual Question AnsweringVQA v2
Accuracy84.7
1165
Visual Question AnsweringTextVQA
Accuracy70.4
1117
Visual Question AnsweringGQA
Accuracy59.43
963
Object Hallucination EvaluationPOPE
Accuracy88
935
Image CaptioningMS COCO Karpathy (test)
CIDEr1.487
682
Visual Question AnsweringVQA v2 (test-dev)
Overall Accuracy82.3
664
Multimodal UnderstandingMM-Vet
MM-Vet Score54.5
418
Referring Expression ComprehensionRefCOCO+ (val)
Accuracy88.7
345
Referring Expression ComprehensionRefCOCO (val)
Accuracy92.8
335
Referring Expression ComprehensionRefCOCO (testA)
Accuracy0.948
333
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