@inproceedings{zhang-etal-2025-weaving,
    title = "Weaving Context Across Images: Improving Vision-Language Models through Focus-Centric Visual Chains",
    author = "Zhang, Juntian  and
      Cheng, Chuanqi  and
      Liu, Yuhan  and
      Liu, Wei  and
      Luan, Jian  and
      Yan, Rui",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1347/",
    pages = "27782--27798",
    ISBN = "979-8-89176-251-0",
    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."
}