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.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Object Hallucination Evaluation | POPE | Accuracy88 | 1455 | |
| Visual Question Answering | VQA v2 | Accuracy84.7 | 1362 | |
| Visual Question Answering | TextVQA | Accuracy70.4 | 1285 | |
| Visual Question Answering | GQA | Accuracy59.43 | 1249 | |
| Visual Question Answering | VQA v2 (test-dev) | Overall Accuracy82.3 | 706 | |
| Image Captioning | MS COCO Karpathy (test) | CIDEr1.487 | 682 | |
| Multimodal Understanding | MM-Vet | MM-Vet Score54.5 | 531 | |
| Multimodal Reasoning | MM-Vet | MM-Vet Score52.8 | 431 | |
| Mathematical Reasoning | MathVista | Score38.6 | 385 | |
| Visual Question Answering | ScienceQA | Accuracy91.2 | 370 |