Croc: Pretraining Large Multimodal Models with Cross-Modal Comprehension

Yin Xie, Kaicheng Yang, Ninghua Yang, Weimo Deng, Xiangzidai, Tiancheng Gu, Yumeng Wang, Xiang An, Yongle Zhao, Ziyong Feng, Jiankang Deng

Keywords: Visual Language Models; Pretraining

Abstract: Recent advancements in Large Language Models (LLMs) have catalyzed the development of Large Multimodal Models (LMMs). However, existing research primarily focuses on tuning language and image instructions, ignoring the critical pretraining phase where models learn to process textual and visual modalities jointly. This paper proposes a new pretraining paradigm for LMMs to enhance the visual comprehension capabilities of LLMs by introducing a novel cross-modal comprehension stage. Specifically, we design a dynamically learnable prompt token pool and employ the Hungarian algorithm to replace part of the original visual tokens with the most relevant prompt tokens. Then, we conceptualize visual tokens as analogous to a "foreign language" for the LLMs and propose a mixed attention mechanism with bidirectional visual attention and unidirectional textual attention to comprehensively enhance the understanding of visual tokens. Meanwhile, we integrate a detailed caption generation task, leveraging rich descriptions to further facilitate LLMs in understanding visual semantic information. After pretraining on 1.5 million publicly accessible data, we present a new foundation model called Croc. Experimental results demonstrate that Croc achieves new state-of-the-art performance on massive vision-language benchmarks. To support reproducibility and facilitate further research, we will release the training code and pre-trained model weights.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 1478