Researchers from Zhipu AI and Tsinghua University have introduced CogVLM, an open-source visual language model that aims to enhance the integration between language and visual information. This model achieves state-of-the-art or near-best performance on various cross-modal benchmarks and is expected to have a positive impact on visual understanding research and applications.
Introducing CogVLM: A Powerful Open-Source Visual Language Foundation Model
Models of visual language are versatile and effective. They can be used for various tasks such as picture captioning, visual question answering, visual grounding, and segmentation. As these models are scaled up, they also improve in other areas like in-context learning. However, training a visual language model from scratch can be challenging. It is more practical to train a visual language model using a pre-trained language model.
The Limitations of Shallow Alignment Techniques
Shallow alignment techniques, like BLIP-2, transfer image characteristics to the language model’s input embedding space using a trainable Q-Former or a linear layer. While this approach converges quickly, it does not perform as well as training the language and vision modules simultaneously. Shallow alignment techniques can result in poor visual comprehension skills and hallucinations in chat-style visual language models.
Enhancing Visual Understanding with CogVLM
CogVLM, developed by researchers from Zhipu AI and Tsinghua University, addresses the limitations of shallow alignment approaches. It emphasizes the deep integration of language and visual information to improve performance. CogVLM enhances the language model with a trainable visual expert, using separate QKV matrices and MLP layers for picture features and text characteristics, respectively. This approach maintains the same computational efficiency while increasing the number of parameters.
The Performance of CogVLM
CogVLM-17B, trained from Vicuna-7B, achieves state-of-the-art or second-best performance on various cross-modal benchmarks, including image captioning, visual question answering, multiple choice, and visual grounding datasets. Additionally, CogVLM-28B-zh, trained from ChatGLM-12B, supports both Chinese and English for commercial use. The open-sourcing of CogVLM is expected to have a significant positive impact on visual understanding research and industrial applications.
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