Collaborative perception is vital for autonomous driving yet remains constrained by tight communication budgets. Earlier work reduced bandwidth by compressing full feature maps with fixed-rate encoders, which adapts poorly to a changing environment, and it further evolved into spatial selection methods that improve efficiency by focusing on salient regions, but this object-centric approach often sacrifices global context, weakening holistic scene understanding. To overcome these limitations, we introduce \textit{WhisperNet}, a bandwidth-aware framework that proposes a novel, receiver-centric paradigm for global coordination across agents. Senders generate lightweight saliency metadata, while the receiver formulates a global request plan that dynamically budgets feature contributions across agents and features, retrieving only the most informative features. A collaborative feature routing module then aligns related messages before fusion to ensure structural consistency. Extensive experiments show that WhisperNet achieves state-of-the-art performance, improving AP@0.7 on OPV2V by 2.4\% with only 0.5\% of the communication cost. As a plug-and-play component, it boosts strong baselines with merely 5\% of full bandwidth while maintaining robustness under localization noise. These results demonstrate that globally-coordinated allocation across \textit{what} and \textit{where} to share is the key to achieving efficient collaborative perception.
Tongji University（同济大学）；Westwell lab（西井实验室）；Technische Universität München（慕尼黑工业大学）
Multi-agent collaborative perception (MCP) has recently attracted much attention. It includes three key processes: communication for sharing, collaboration for integration, and reconstruction for different downstream tasks. Existing methods pursue designing the collaboration process alone, ignoring their intrinsic interactions and resulting in suboptimal performance. In contrast, we aim to propose a Unified Collaborative perception framework named UMC, optimizing the communication, collaboration, and reconstruction processes with the Multi-resolution technique. The communication introduces a n…
CoVStream: Edge-Cloud Collaboration for Understanding of Long Video Streams
The State Key Lab of Brain-Machine Intelligence, Zhejiang University（浙江大学脑机智能国家重点实验室）；vivo Mobile Communication Co., Ltd., Shenzhen, China（深圳 vivo 通信有限公司）
New York University（纽约大学）；Shanghai Jiao Tong University（上海交通大学）；Rutgers University（罗格斯大学）
To promote better performance-bandwidth trade-off for multi-agent perception, we propose a novel distilled collaboration graph (DiscoGraph) to model trainable, pose-aware, and adaptive collaboration among agents. Our key novelties lie in two aspects. First, we propose a teacher-student framework to train DiscoGraph via knowledge distillation. The teacher model employs an early collaboration with holistic-view inputs; the student model is based on intermediate collaboration with single-view inputs. Our framework trains DiscoGraph by constraining post-collaboration feature maps in the student mo…
Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and Beyond
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai AI Laboratory（上海人工智能实验室）
INSTINCT: Instance-Level Interaction Architecture for Query-Based Collaborative Perception
School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）；School of Future Science and Engineering, Soochow University（苏州大学未来科学与工程学院）
V2X-PC: Vehicle-to-everything Collaborative Perception via Point Cluster
Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）；Cooperative Medianet Innovation Center (CMIC), Shanghai Jiao Tong University（上海交通大学合作 medianet 创新中心）；Shanghai AI Laboratory（上海人工智能实验室）；Sangfor Technologies Inc.（深信服科技股份有限公司）
