CogVLM2: Visual Language Models for Image and Video Understanding
<|reference_start|>CogVLM2: Visual Language Models for Image and Video Understanding: Beginning with VisualGLM and CogVLM, we are continuously exploring VLMs in pursuit of enhanced vision-language fusion, efficient higher-resolution architecture, and broader modalities and applications. Here we propose the CogVLM2 family, a new generation of visual language models for image and video understanding including CogVLM2, CogVLM2-Video and GLM-4V. As an image understanding model, CogVLM2 inherits the visual expert architecture with improved training recipes in both pre-training and post-training stages, supporting input resolution up to $1344 \times 1344$ pixels. As a video understanding model, CogVLM2-Video integrates multi-frame input with timestamps and proposes automated temporal grounding data construction. Notably, CogVLM2 family has achieved state-of-the-art results on benchmarks like MMBench, MM-Vet, TextVQA, MVBench and VCGBench. All models are open-sourced in https://github.com/THUDM/CogVLM2 and https://github.com/THUDM/GLM-4, contributing to the advancement of the field.<|reference_end|>
@article{hong2024cogvlm2:,
    title={CogVLM2: Visual Language Models for Image and Video Understanding},
    author={Wenyi Hong, Weihan Wang, Ming Ding, Wenmeng Yu, Qingsong Lv, Yan Wang,
  Yean Cheng, Shiyu Huang, Junhui Ji, Zhao Xue, Lei Zhao, Zhuoyi Yang, Xiaotao
  Gu, Xiaohan Zhang, Guanyu Feng, Da Yin, Zihan Wang, Ji Qi, Xixuan Song, Peng
  Zhang, Debing Liu, Bin Xu, Juanzi Li, Yuxiao Dong, Jie Tang},
    journal={arXiv preprint arXiv:2408.16500},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16500},
    primaryClass={cs.CV}
}