MARS2 2025多模態推理挑戰賽：數據集、方法、成果、討論與展望
MARS2 2025 Challenge on Multimodal Reasoning: Datasets, Methods, Results, Discussion, and Outlook
September 17, 2025
作者: Peng Xu, Shengwu Xiong, Jiajun Zhang, Yaxiong Chen, Bowen Zhou, Chen Change Loy, David A. Clifton, Kyoung Mu Lee, Luc Van Gool, Ruiming He, Ruilin Yao, Xinwei Long, Jirui Huang, Kai Tian, Sa Yang, Yihua Shao, Jin Feng, Yue Zhong, Jiakai Zhou, Cheng Tang, Tianyu Zou, Yifang Zhang, Junming Liang, Guoyou Li, Zhaoxiang Wang, Qiang Zhou, Yichen Zhao, Shili Xiong, Hyeongjin Nam, Jaerin Lee, Jaeyoung Chung, JoonKyu Park, Junghun Oh, Kanggeon Lee, Wooseok Lee, Juneyoung Ro, Turghun Osman, Can Hu, Chaoyang Liao, Cheng Chen, Chengcheng Han, Chenhao Qiu, Chong Peng, Cong Xu, Dailin Li, Feiyu Wang, Feng Gao, Guibo Zhu, Guopeng Tang, Haibo Lu, Han Fang, Han Qi, Hanxiao Wu, Haobo Cheng, Hongbo Sun, Hongyao Chen, Huayong Hu, Hui Li, Jiaheng Ma, Jiang Yu, Jianing Wang, Jie Yang, Jing He, Jinglin Zhou, Jingxuan Li, Josef Kittler, Lihao Zheng, Linnan Zhao, Mengxi Jia, Muyang Yan, Nguyen Thanh Thien, Pu Luo, Qi Li, Shien Song, Shijie Dong, Shuai Shao, Shutao Li, Taofeng Xue, Tianyang Xu, Tianyi Gao, Tingting Li, Wei Zhang, Weiyang Su, Xiaodong Dong, Xiao-Jun Wu, Xiaopeng Zhou, Xin Chen, Xin Wei, Xinyi You, Xudong Kang, Xujie Zhou, Xusheng Liu, Yanan Wang, Yanbin Huang, Yang Liu, Yang Yang, Yanglin Deng, Yashu Kang, Ye Yuan, Yi Wen, Yicen Tian, Yilin Tao, Yin Tang, Yipeng Lin, Yiqing Wang, Yiting Xi, Yongkang Yu, Yumei Li, Yuxin Qin, Yuying Chen, Yuzhe Cen, Zhaofan Zou, Zhaohong Liu, Zhehao Shen, Zhenglin Du, Zhengyang Li, Zhenni Huang, Zhenwei Shao, Zhilong Song, Zhiyong Feng, Zhiyu Wang, Zhou Yu, Ziang Li, Zihan Zhai, Zijian Zhang, Ziyang Peng, Ziyun Xiao, Zongshu Li
cs.AI
摘要
本文回顧了MARS2 2025多模態推理挑戰賽。我們旨在通過一個大型基準測試，匯聚多模態機器學習與大型語言模型（LLMs）的不同方法，以期讓研究人員更好地跟蹤這一極具活力領域的最新進展。與此同時，日益增多的測試平台推動了通用大型語言模型的發展。因此，今年的MARS2聚焦於現實世界和專業場景，以拓寬多模態語言模型（MLLMs）的推理應用。我們的組織團隊發布了兩個定製數據集Lens和AdsQA作為測試集，分別支持12種日常場景中的通用推理和廣告視頻中的領域特定推理。我們評估了包含通用MLLMs和任務特定模型在內的40多個基線模型，並開設了三個競賽賽道，即現實場景中的視覺定位（VG-RS）、具備空間意識的視覺問答（VQA-SA）以及創意廣告視頻中的視覺推理（VR-Ads）。最終，來自知名學術和工業機構的76支團隊報名參賽，超過40份有效提交（總提交量超過1200份）被納入我們的排名列表。我們的數據集、代碼集（40多個基線模型和15多個參賽者的方法）以及排名已在MARS2研討會網站和我們的GitHub組織頁面https://github.com/mars2workshop/上公開，我們將持續提供更新和即將舉辦活動的公告。
English
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.