Towards Self-Improving Systematic Cognition for Next-Generation
  Foundation MLLMs
Authors: name: Xiaoying Zhang, name: Da Peng, name: Yipeng Zhang, name: Zonghao Guo, name: Chengyue Wu, name: Chi Chen, name: Wei Ke, name: Helen Meng, name: Maosong Sun

Despite their impressive capabilities, Multimodal Large Language Models
(MLLMs) face challenges with fine-grained perception and complex reasoning.
Prevalent multimodal pre-training approaches focus on enhancing perception by
training on high-quality image captions due to the extremely high cost of
collecting chain-of-thought (CoT) reasoning data for improving reasoning. While
leveraging advanced MLLMs for caption generation enhances scalability, the
outputs often lack comprehensiveness and accuracy. In this paper, we introduce
Self-Improving cognition (SIcog), a self-learning framework designed to
construct next-generation foundation MLLMs by enhancing their systematic
cognitive capabilities through multimodal pre-training with self-generated
data. Specifically, we propose Chain-of-Description, an approach that improves
an MLLM's systematic perception by enabling step-by-step visual understanding,
ensuring greater comprehensiveness and accuracy. Additionally, we adopt a
structured CoT reasoning technique to enable MLLMs to integrate in-depth
multimodal reasoning. To construct a next-generation foundation MLLM with
self-improved cognition, SIcog first equips an MLLM with systematic perception
and reasoning abilities using minimal external annotations. The enhanced models
then generate detailed captions and CoT reasoning data, which are further
curated through self-consistency. This curated data is ultimately used for
multimodal pre-training to develop next-generation foundation models. Extensive
experiments on both low- and high-resolution MLLMs across diverse benchmarks
demonstrate that, with merely 213K self-generated pre-training samples, SIcog
produces next-generation foundation MLLMs with significantly improved
cognition, achieving benchmark-leading performance compared to prevalent
pre-training approaches.