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