Migician: Revealing the Magic of Free-Form Multi-Image Grounding in
  Multimodal Large Language Models
Authors: name: You Li; name: Heyu Huang; name: Chi Chen; name: Kaiyu Huang; name: Chao Huang; name: Zonghao Guo; name: Zhiyuan Liu; name: Jinan Xu; name: Yuhua Li; name: Ruixuan Li; name: Maosong Sun
Published: 2025-01-10 07:56:23

Abstract: The recent advancement of Multimodal Large Language Models (MLLMs) has
significantly improved their fine-grained perception of single images and
general comprehension across multiple images. However, existing MLLMs still
face challenges in achieving precise grounding in complex multi-image
scenarios. To address this, we first explore a Chain-of-Thought (CoT) framework
that integrates single-image grounding with multi-image comprehension. While
partially effective, it remains unstable and struggles to capture abstract
visual information due to its non-end-to-end nature. Therefore, we introduce
Migician, the first multi-image grounding model capable of performing free-form
and accurate grounding across multiple images. To support this, we present the
MGrounding-630k dataset, which comprises data for several multi-image grounding
tasks derived from existing datasets, along with newly generated free-form
grounding instruction-following data. Furthermore, we propose MIG-Bench, a
comprehensive benchmark specifically designed for evaluating multi-image
grounding capabilities. Experimental results demonstrate that our model
achieves significantly superior multi-image grounding capabilities,
outperforming the best existing MLLMs by 21.61% and even surpassing much larger
70B models. Our code, model, dataset, and benchmark are fully open-sourced.