MARS2 2025 Challenge on Multimodal Reasoning: Datasets, Methods,
  Results, Discussion, and Outlook
This paper reviews the MARS2 2025 Challenge on Multimodal Reasoning. We aim
to bring together different approaches in multimodal machine learning and LLMs
via a large benchmark. We hope it better allows researchers to follow the
state-of-the-art in this very dynamic area. Meanwhile, a growing number of
testbeds have boosted the evolution of general-purpose large language models.
Thus, this year's MARS2 focuses on real-world and specialized scenarios to
broaden the multimodal reasoning applications of MLLMs. Our organizing team
released two tailored datasets Lens and AdsQA as test sets, which support
general reasoning in 12 daily scenarios and domain-specific reasoning in
advertisement videos, respectively. We evaluated 40+ baselines that include
both generalist MLLMs and task-specific models, and opened up three competition
tracks, i.e., Visual Grounding in Real-world Scenarios (VG-RS), Visual Question
Answering with Spatial Awareness (VQA-SA), and Visual Reasoning in Creative
Advertisement Videos (VR-Ads). Finally, 76 teams from the renowned academic and
industrial institutions have registered and 40+ valid submissions (out of
1200+) have been included in our ranking lists. Our datasets, code sets (40+
baselines and 15+ participants' methods), and rankings are publicly available
on the MARS2 workshop website and our GitHub organization page
https://github.com/mars2workshop/, where our updates and announcements of
upcoming events will be continuously provided.
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Peng Xu
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Shengwu Xiong
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Jiajun Zhang
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Yaxiong Chen
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Bowen Zhou
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Chen Change Loy
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David A. Clifton
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Kyoung Mu Lee
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Luc Van Gool
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Ruiming He
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Ruilin Yao
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Xinwei Long
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Jirui Huang
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Kai Tian
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Sa Yang
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Yihua Shao
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Jin Feng
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Yue Zhong
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Jiakai Zhou
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Cheng Tang
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Tianyu Zou
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Yifang Zhang
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Junming Liang
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Guoyou Li
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Zhaoxiang Wang
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Qiang Zhou
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Yichen Zhao
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Shili Xiong
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Hyeongjin Nam
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Jaerin Lee
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Jaeyoung Chung
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JoonKyu Park
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Junghun Oh
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Kanggeon Lee
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Wooseok Lee
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Juneyoung Ro
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Turghun Osman
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Can Hu
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Chaoyang Liao
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Cheng Chen
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Chengcheng Han
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Chenhao Qiu
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Chong Peng
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Cong Xu
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Dailin Li
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Feiyu Wang
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Feng Gao
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Guibo Zhu
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Guopeng Tang
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Haibo Lu
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Han Fang
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Han Qi
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Hanxiao Wu
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Haobo Cheng
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Hongbo Sun
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Hongyao Chen
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Huayong Hu
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Hui Li
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Jiaheng Ma
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Jiang Yu
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Jianing Wang
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Jie Yang
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Jing He
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Jinglin Zhou
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Jingxuan Li
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Josef Kittler
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Lihao Zheng
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Linnan Zhao
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Mengxi Jia
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Muyang Yan
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Nguyen Thanh Thien
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Pu Luo
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Qi Li
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Shien Song
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Shijie Dong
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Shuai Shao
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Shutao Li
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Taofeng Xue
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Tianyang Xu
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Tianyi Gao
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Tingting Li
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Wei Zhang
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Weiyang Su
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Xiaodong Dong
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Xiao-Jun Wu
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Xiaopeng Zhou
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Xin Chen
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Xin Wei
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Xinyi You
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Xudong Kang
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Xujie Zhou
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Xusheng Liu
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Yanan Wang
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Yanbin Huang
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Yang Liu
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Yang Yang
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Yanglin Deng
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Yashu Kang
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Ye Yuan
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Yi Wen
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Yicen Tian
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Yilin Tao
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Yin Tang
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Yipeng Lin
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Yiqing Wang
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Yiting Xi
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Yongkang Yu
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Yumei Li
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Yuxin Qin
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Yuying Chen
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Yuzhe Cen
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Zhaofan Zou
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Zhaohong Liu
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Zhehao Shen
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Zhenglin Du
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Zhengyang Li
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Zhenni Huang
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Zhenwei Shao
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Zhilong Song
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Zhiyong Feng
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Zhiyu Wang
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Zhou Yu
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Ziang Li
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Zihan Zhai
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Zijian Zhang
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Ziyang Peng
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Ziyun Xiao
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Zongshu Li
MARS2 2025 Challenge on Multimodal Reasoning: Datasets, Methods,
  Results, Discussion, and Outlook
This paper reviews the MARS2 2025 Challenge on Multimodal Reasoning. We aim
to bring together different approaches in multimodal machine learning and LLMs
via a large benchmark. We hope it better allows researchers to follow the
state-of-the-art in this very dynamic area. Meanwhile, a growing number of
testbeds have boosted the evolution of general-purpose large language models.
Thus, this year's MARS2 focuses on real-world and specialized scenarios to
broaden the multimodal reasoning applications of MLLMs. Our organizing team
released two tailored datasets Lens and AdsQA as test sets, which support
general reasoning in 12 daily scenarios and domain-specific reasoning in
advertisement videos, respectively. We evaluated 40+ baselines that include
both generalist MLLMs and task-specific models, and opened up three competition
tracks, i.e., Visual Grounding in Real-world Scenarios (VG-RS), Visual Question
Answering with Spatial Awareness (VQA-SA), and Visual Reasoning in Creative
Advertisement Videos (VR-Ads). Finally, 76 teams from the renowned academic and
industrial institutions have registered and 40+ valid submissions (out of
1200+) have been included in our ranking lists. Our datasets, code sets (40+
baselines and 15+ participants' methods), and rankings are publicly available
on the MARS2 workshop website and our GitHub organization page
https://github.com/mars2workshop/, where our updates and announcements of
upcoming events will be continuously provided.
The MARS2 2025 Challenge focuses on multimodal reasoning with large language models through real-world and specialized scenarios, evaluating 40+ models across three competition tracks using tailored datasets.
multimodal machine learning LLMs MARS2 multimodal reasoning large language models testbeds general-purpose large language models real-world scenarios specialized scenarios multimodal reasoning applications MLLMs Visual Grounding in Real-world Scenarios Visual Question Answering with Spatial Awareness Visual Reasoning in Creative Advertisement Videos datasets code sets GitHub organization page