title: Migician: Revealing the Magic of Free-Form Multi-Image Grounding in
    Multimodal Large Language Models
summary: 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.
paper_authors._id: 67850070609833baf1d0c8f6
paper_authors.name: You Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-14T08:29:04.061Z
paper_authors._id: 654f3e104c8874c64d43aafa
paper_authors.avatarUrl: /avatars/00de263f98a81c52cdb321fb11b16c06.svg
paper_authors.fullname: You Li
paper_authors.type: user
paper_authors.user: Michael4933
paper_authors._id: 67850070609833baf1d0c8f7
paper_authors.name: Heyu Huang
paper_authors._id: 67850070609833baf1d0c8f8
paper_authors.name: Chi Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-14T08:29:06.103Z
paper_authors._id: 642086ed290342c5df85662d
paper_authors.avatarUrl: /avatars/915a4d7b89455ae97b8544c79286ddf8.svg
paper_authors.fullname: Chi Chen
paper_authors.type: user
paper_authors.user: carboncoo
paper_authors._id: 67850070609833baf1d0c8f9
paper_authors.name: Kaiyu Huang
paper_authors._id: 67850070609833baf1d0c8fa
paper_authors.name: Chao Huang
paper_authors._id: 67850070609833baf1d0c8fb
paper_authors.name: Zonghao Guo
paper_authors._id: 67850070609833baf1d0c8fc
paper_authors.name: Zhiyuan Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:54:20.269Z
paper_authors._id: 6472f1f7485a7c8e1bcf507b
paper_authors.avatarUrl: /avatars/79dca137583875191ea883b193b630b7.svg
paper_authors.fullname: Zhiyuan Liu
paper_authors.type: user
paper_authors.user: zibuyu9
paper_authors._id: 67850070609833baf1d0c8fd
paper_authors.name: Jinan Xu
paper_authors._id: 67850070609833baf1d0c8fe
paper_authors.name: Yuhua Li
paper_authors._id: 67850070609833baf1d0c8ff
paper_authors.name: Ruixuan Li
paper_authors._id: 67850070609833baf1d0c900
paper_authors.name: Maosong Sun
paper_title: Migician: Revealing the Magic of Free-Form Multi-Image Grounding in
    Multimodal Large Language Models
paper_summary: 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.
paper_ai_summary: Migician, a multi-image grounding model, outperforms existing MLLMs in complex multi-image scenarios using the MGrounding-630k dataset and MIG-Bench benchmark.
paper_ai_keywords: Multimodal Large Language Models Chain-of-Thought Migician multi-image grounding MGrounding-630k dataset MIG-Bench free-form grounding