Chimera: Improving Generalist Model with Domain-Specific Experts
Authors: name: Tianshuo Peng, name: Mingsheng Li, name: Hongbin Zhou, name: Renqiu Xia, name: Renrui Zhang, name: Lei Bai, name: Song Mao, name: Bin Wang, name: Conghui He, name: Aojun Zhou, name: Botian Shi, name: Tao Chen, name: Bo Zhang, name: Xiangyu Yue

Recent advancements in Large Multi-modal Models (LMMs) underscore the
importance of scaling by increasing image-text paired data, achieving
impressive performance on general tasks. Despite their effectiveness in broad
applications, generalist models are primarily trained on web-scale datasets
dominated by natural images, resulting in the sacrifice of specialized
capabilities for domain-specific tasks that require extensive domain prior
knowledge. Moreover, directly integrating expert models tailored for specific
domains is challenging due to the representational gap and imbalanced
optimization between the generalist model and experts. To address these
challenges, we introduce Chimera, a scalable and low-cost multi-modal pipeline
designed to boost the ability of existing LMMs with domain-specific experts.
Specifically, we design a progressive training strategy to integrate features
from expert models into the input of a generalist LMM. To address the
imbalanced optimization caused by the well-aligned general visual encoder, we
introduce a novel Generalist-Specialist Collaboration Masking (GSCM) mechanism.
This results in a versatile model that excels across the chart, table, math,
and document domains, achieving state-of-the-art performance on multi-modal
reasoning and visual content extraction tasks, both of which are challenging
tasks for assessing existing LMMs.