In V2X collaborative perception, the domain gaps between heterogeneous nodes pose a significant challenge for effective information fusion. Pose errors arising from latency and GPS localization noise further exacerbate the issue by leading to feature misalignment. To overcome these challenges, we propose V2X-DGPE, a high-accuracy and robust V2X feature-level collaborative perception framework. V2X-DGPE employs a Knowledge Distillation Framework and a Feature Compensation Module to learn domain-invariant representations from multi-source data, effectively reducing the feature distribution gap between vehicles and roadside infrastructure. Historical information is utilized to provide the model with a more comprehensive understanding of the current scene. Furthermore, a Collaborative Fusion Module leverages a heterogeneous self-attention mechanism to extract and integrate heterogeneous representations from vehicles and infrastructure. To address pose errors, V2X-DGPE introduces a deformable attention mechanism, enabling the model to adaptively focus on critical parts of the input features by dynamically offsetting sampling points. Extensive experiments on the real-world DAIR-V2X dataset demonstrate that the proposed method outperforms existing approaches, achieving state-of-the-art detection performance. The code is available at https://github.com/wangsch10/V2X-DGPE.
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；Zhejiang University（浙江大学）
Pseudo supervision is regarded as the core idea in semi-supervised learning for semantic segmentation, and there is always a tradeoff between utilizing only the high-quality pseudo labels and leveraging all the pseudo labels. Addressing that, we propose a novel learning approach, called Conservative-Progressive Collaborative Learning (CPCL), among which two predictive networks are trained in parallel, and the pseudo supervision is implemented based on both the agreement and disagreement of the two predictions. One network seeks common ground via intersection supervision and is supervised by th…
Addressing Domain Discrepancy: A Dual-branch Collaborative Model to Unsupervised Dehazing
College of Computer Science and Engineering, Chongqing University of Technology（重庆理工大学计算机科学与工程学院）；Industrial Training Center, Guangdong Polytechnic Normal University（广东技术师范大学工业训练中心）
Disco: Densely-overlapping Cell Instance Segmentation via Adjacency-aware Collaborative Coloring
Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）；Fudan University（复旦大学）；Shanghai Innovation Institute（上海创新研究院）；School of Life Sciences and Biotechnology, Shanghai Jiao Tong University（上海交通大学生命科学与生物技术学院）；Lingang Laboratory（临港实验室）；Renji Hospital, School of Medicine, Shanghai Jiao Tong University（仁济医院，上海交通大学医学院）
Tianjin University（天津大学）；Binghamton University, State University of New York（纽约州立大学宾汉姆顿分校）
General Continual Learning (GCL) aims at learning from non independent and identically distributed stream data without catastrophic forgetting of the old tasks that don't rely on task boundaries during both training and testing stages. We reveal that the relation and feature deviations are crucial problems for catastrophic forgetting, in which relation deviation refers to the deficiency of the relationship among all classes in knowledge distillation, and feature deviation refers to indiscriminative feature representations. To this end, we propose a Complementary Calibration (CoCa) framework by…
Weakly Supervised Visible-Infrared Person Re-Identification via Heterogeneous Expert Collaborative Consistency Learning
Faculty of Information Engineering and Automation, Kunming University of Science and Technology（昆明理工大学信息工程与自动化学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）
Collaborative Semantic Aggregation and Calibration for Federated Domain Generalization
Zhejiang University（浙江大学）；Key Laboratory for Corneal Diseases Research of Zhejiang Province（浙江省角膜病研究重点实验室）；Shanghai Institute for Advanced Study of Zhejiang University（浙江大学上海高等研究院）；Shanghai AI Laboratory（上海人工智能实验室）
InScope: A New Real-world 3D Infrastructure-side Collaborative Perception Dataset for Open Traffic Scenarios
School of Intelligent Systems Engineering, Sun Yat-sen University（智能系统工程学院，中山大学）；Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)（广东海洋科学与工程实验室（珠海））
An Architecture Built for Federated Learning: Addressing Data Heterogeneity through Adaptive Normalization-Free Feature Recalibration
Department of Computer Science, City St. George’s, University of London（伦敦大学城市圣乔治学院计算机科学系）；BioMedIA, Department of Computing, Imperial College London（伦敦帝国理工学院计算系BioMedIA）
