Multi-Modal Decouple and Recouple Network for Robust 3D Object Detection
State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（人机混合增强智能国家重点实验室，人工智能与机器人研究院，西安交通大学）；Intelligent Transportation Thrust of the Systems Hub, The Hong Kong University of Science and Technology (Guangzhou)（系统枢纽智能交通方向，香港科技大学（广州））；Multimodal Experiences Research Lab, Dolby Laboratories（多模态体验研究实验室，Dolby实验室）
Rui Ding, Zhaonian Kuang, Yuzhe Ji, Meng Yang, Xinhu Zheng, Gang Hua
Multi-modal 3D object detection with bird's eye view (BEV) has achieved desired advances on benchmarks. Nonetheless, the accuracy may drop significantly in the real world due to data corruption such as sensor configurations for LiDAR and scene conditions for camera. One design bottleneck of previous models resides in the tightly coupling of multi-modal BEV features during fusion, which may degrade the overall system performance if one modality or both is corrupted. To mitigate, we propose a Multi-Modal Decouple and Recouple Network for robust 3D object detection under data corruption. Different modalities commonly share some high-level invariant features. We observe that these invariant features across modalities do not always fail simultaneously, because different types of data corruption affect each modality in distinct ways.These invariant features can be recovered across modalities for robust fusion under data corruption.To this end, we explicitly decouple Camera/LiDAR BEV features into modality-invariant and modality-specific parts. It allows invariant features to compensate each other while mitigates the negative impact of a corrupted modality on the other.We then recouple these features into three experts to handle different types of data corruption, respectively, i.e., LiDAR, camera, and both.For each expert, we use modality-invariant features as robust information, while modality-specific features serve as a complement.Finally, we adaptively fuse the three experts to exact robust features for 3D object detection. For validation, we collect a benchmark with a large quantity of data corruption for LiDAR, camera, and both based on nuScenes. Our model is trained on clean nuScenes and tested on all types of data corruption. Our model consistently achieves the best accuracy on both corrupted and clean data compared to recent models.
De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation
School of Computer Science, University of Nottingham Ningbo China（诺丁汉大学宁波校区计算机科学学院）；Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）；School of School of Engineering & Physical Sciences, University of Lincoln（林肯大学工程与物理科学学院）；Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港科学与创新研究院）；School of Computer Science, University of Nottingham（诺丁汉大学计算机科学学院）；The Hong Kong Polytechnic University（香港理工大学）
DPDETR: Decoupled Position Detection Transformer for Infrared-Visible Object Detection
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications（通信与信息工程学院，重庆邮电大学）；School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University（智能系统工程学院，中山大学深圳校区）；School of Mathematics and Statistics, Xi’an Jiaotong University（数学与统计学院，西安交通大学）
Beihang University（北京航空航天大学）；State Key Laboratory of High-Efficiency Reusable Aerospace Transportation Technology（高效可重复使用航天运输技术国家重点实验室）；The School of Astronautics（航天学院）；The National Key Laboratory of Integrated Air-Ground Navigation Technologies（集成空天地导航技术国家实验室）；The State Key Laboratory of Media Convergence and Communication（媒体融合与传播国家重点实验室）；The School of Artificial Intelligence（人工智能学院）；Zhongguancun Academy（中关村学院）；The School of Automation Science and Electrical Engineering（自动化科学与电气工程学院）；Beijing Institute of Control and Electronics Technology（北京控制电子技术研究所）；Ningbo Institute of Digital Twin（宁波数字孪生研究所）；Eastern Institute of Technology（东部技术研究所）；Hangzhou Research Institute, School of Artificial Intelligence（杭州研究院，人工智能学院）
研究基于双流 CNN 的 RGB-IR 目标检测问题，提出拉普拉斯解耦特征增强模块 LDFE，通过全局-局部分解等操作融合不同阶段特征，在多个数据集上显著提升目标检测性能，mAP 超 SOTA 方法。
DEYOLO: Dual-Feature-Enhancement YOLO for Cross-Modality Object Detection
College of Artificial Intelligence, Nankai University（南开大学人工智能学院）；Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University（南开大学教育部可信行为智能工程研究中心）
M^2C-EvDet: Multi-Domain Multi-Order Cross-Modal Knowledge Distillation for Event-based Object Detection
BNRist, THUIBCS, BLBCI, School of Software, Tsinghua University（清华大学软件学院、北京信息科学与技术国家研究中心、清华-英特尔先进计算与智能技术联合研究中心、北京国家区块链与物联网技术研究中心）；Yangtze Delta Region Institute, Tsinghua University（清华大学长三角研究院）；School of Economics and Management, Beijing Forestry University（北京林业大学经济管理学院）
Rethinking Unsupervised Cross-modal Flow Estimation: Learning from Decoupled Optimization and Consistency Constraint
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；Ningbo Global Innovation Center, Zhejiang University（浙江大学宁波全球创新中心）；NingboTech University（宁波科技大学）
Decoupled Hierarchical Distillation for Multimodal Emotion Recognition
School of Computer Science and Engineering, and the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Southeast University（计算机科学与工程学院，新一代人工智能技术及跨学科应用重点实验室，东南大学）；School of Computer Science and Engineering, Nanjing University of Science and Technology（计算机科学与工程学院，南京理工大学）；Institute of Intelligent Information Processing, Taizhou University（智能信息处理研究院，台州大学）；School of Engineering Science, University of Chinese Academy of Sciences（工程科学学院，中国科学院大学）；Peng Cheng Laboratory（鹏城实验室）；School of Computer Science and Engineering, Nanyang Technological University（计算机科学与工程学院，南洋理工大学）
Decoupled Cross-Modal Alignment Network for Text-RGBT Person Retrieval and A High-Quality Benchmark
School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；National Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology（光电信息采集与防护技术国家重点实验室）；Anhui Provincial Key Laboratory of Multimodal Cognitive Computation（安徽省多模态认知计算重点实验室）
Decouple, Reorganize, and Fuse: A Multimodal Framework for Cancer Survival Prediction
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；State Key Laboratory of Transvascular Implantation Devices of the Second Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院第二附属医院血管植入设备国家重点实验室）；Transvascular Implantation Devices Research Institute（血管植入设备研究院）；Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence（浙江省医学影像人工智能重点实验室）；China Mobile (Zhejiang) Research & Innovation Institute（中国移动（浙江）研究院）
Prototype-Based Information Compensation Network for Multi-Source Remote Sensing Data Classification
State Key Laboratory of Physical Oceanography, Ocean University of China（海洋大学物理海洋国家重点实验室）；Department of Electrical and Computer Engineering, Mississippi State University（密苏里州立大学电气与计算机工程系）
Decouple before Align: Visual Disentanglement Enhances Prompt Tuning
Cooperative Medianet Innovation Center, Shanghai Jiao Tong University（合作中位网创新中心，上海交通大学）；School of Artificial Intelligence, Shanghai Jiao Tong University（人工智能学院，上海交通大学）；Shanghai Innovation Institute（上海创新研究院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Beijing Institute of Technology（北京理工大学）；A*STAR Centre for Frontier AI Research（A*STAR前沿人工智能研究中心）
Completed Feature Disentanglement Learning for Multimodal MRIs Analysis
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；Medical School of Tianjin University（天津大学医学院）；Peng Cheng Lab（鹏城实验室）；The University of Hong Kong（香港大学）；Tianjin Huanhu Hospital（天津环湖医院）
Air Quality Prediction with A Meteorology-Guided Modality-Decoupled Spatio-Temporal Network
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institution of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Huawei Cloud Enterprise Intelligence（华为云企业智能）
Cross-Modal Fusion Distillation for Fine-Grained Sketch-Based Image Retrieval
University of Exeter（埃克塞特大学）；University of Tübingen（图宾根大学）；MPI for Informatics（马克斯·普朗克信息学研究所）；MPI for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Surrey（萨里大学）
MODAL: Multi-Modal Object Re-ID via Model-Driven Sparse Decoupling and Text-Image Differential Filtering
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机科学与技术学院）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）
Exploring Multi-Modal Control in Music-Driven Dance Generation
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Peng Cheng Laboratory（鹏城实验室）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）
