Reasoning About Human-Object Interactions Through Dual Attention Networks
University of California, Berkeley（加州大学伯克利分校）；MIT-IBM Watson AI Lab, IBM Research（麻省理工学院-IBM沃森人工智能实验室，IBM研究院）；Massachusetts Institute of Technology（麻省理工学院）；The Chinese University of Hong Kong（香港中文大学）
Objects are entities we act upon, where the functionality of an object is determined by how we interact with it. In this work we propose a Dual Attention Network model which reasons about human-object interactions. The dual-attentional framework weights the important features for objects and actions respectively. As a result, the recognition of objects and actions mutually benefit each other. The proposed model shows competitive classification performance on the human-object interaction dataset Something-Something. Besides, it can perform weak spatiotemporal localization and affordance segmentation, despite being trained only with video-level labels. The model not only finds when an action is happening and which object is being manipulated, but also identifies which part of the object is being interacted with. Project page: \url{https://dual-attention-network.github.io/}.
PixDLM: A Dual-Path Multimodal Language Model for UAV Reasoning Segmentation
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（中国教育部多媒体可信感知与高效计算重点实验室，厦门大学）
RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images
Beijing Jiaotong University（北京交通大学）；City University of Hong Kong（香港城市大学）；Hunan University（湖南大学）；Peng Cheng Laboratory（鹏城实验室）；Sun Yat-sen University（中山大学）；City University of Hong Kong Shenzhen Research Institute（香港城市大学深圳研究院）
ARK: A Dual-Axis Multimodal Retrieval Benchmark along Reasoning and Knowledge
College of Computer Science, Sichuan University, Chengdu, China（四川大学计算机学院）；National Key Laboratory of Fundamental Algorithms and Models for Engineering Simulation, Sichuan University, China（四川省工程仿真基础算法与模型国家重点实验室）
DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question Answering
Nanjing University of Posts and Telecommunications（南京邮电大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
DualVLA: Building a Generalizable Embodied Agent via Partial Decoupling of Reasoning and Action
MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China（脑启发智能感知与认知国家重点实验室，中国科学技术大学）；State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，北京大学计算机学院）；CUHK（香港大学）
VITAL: Visual-Semantic Dual Supervision for Enhanced and Interpretable Latent Reasoning in Medical MLLMs
Zhejiang University（浙江大学）；Shanghai AI Laboratory（上海人工智能实验室）；Tencent（腾讯）；Ningbo Global Innovation Center, Zhejiang University（宁波全球创新中心，浙江大学）；Zhejiang Key Laboratory of Digital-Intelligence Service Technology（浙江省数字智能服务技术重点实验室）
Mind What Matters for Reasoning: Aligning Cross-Modal Attention via Selective Probability Mass Concentration
University of Technology Sydney（悉尼科技大学）；East China Normal University（华东师范大学）；The University of New South Wales（新南威尔士大学）；Shanghai Jiaotong University（上海交通大学）；The Education University of Hong Kong（香港教育大学）
Faithful-MR1: Faithful Multimodal Reasoning via Anchoring and Reinforcing Visual Attention
AMAP, Alibaba Group（阿里云实验室，阿里巴巴集团）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）；Nanyang Technological University（南洋理工大学）
From Narrow to Panoramic Vision: Attention-Guided Cold-Start Reshapes Multimodal Reasoning
Tsinghua University（清华大学）；University of South California（南加州大学）；Qwen Team, Alibaba Group（通义实验室，阿里巴巴集团）；University of California San Diego（南加州大学圣地亚哥分校）；Zhejiang University（浙江大学）；Shanghai Jiao Tong University（上海交通大学）
Understanding the computational demands underlying visual reasoning
Artificial and Natural Intelligence Toulouse Institute（图卢兹人工智能与自然智能研究所）；Université de Toulouse（图卢兹大学）；Centre de Recherche Cerveau & Cognition (CerCo), CNRS（法国国家科学研究中心大脑与认知研究中心）
