Weaving Context Across Images: Improving Vision-Language Models through Focus-Centric Visual Chains
Journal:
arXiv
Published Date:
Apr 28, 2025
Abstract
Vision-language models (VLMs) achieve remarkable success in single-image
tasks. However, real-world scenarios often involve intricate multi-image
inputs, leading to a notable performance decline as models struggle to
disentangle critical information scattered across complex visual features. In
this work, we propose Focus-Centric Visual Chain, a novel paradigm that
enhances VLMs'perception, comprehension, and reasoning abilities in multi-image
scenarios. To facilitate this paradigm, we propose Focus-Centric Data
Synthesis, a scalable bottom-up approach for synthesizing high-quality data
with elaborate reasoning paths. Through this approach, We construct VISC-150K,
a large-scale dataset with reasoning data in the form of Focus-Centric Visual
Chain, specifically designed for multi-image tasks. Experimental results on
seven multi-image benchmarks demonstrate that our method achieves average
performance gains of 3.16% and 2.24% across two distinct model architectures,
without compromising the general vision-language capabilities. our study
represents a significant step toward more robust and capable vision-language
systems that can handle complex visual scenarios.
