PEARL is a lightweight, prompt‑embedding framework designed for real‑time, anonymous, and heterogeneous collaborative perception. It uses two parallel, low‑rank visual prompt interpreters—sparse‑detection (LWSD) and dense, domain‑invariant (LWDDI)—to align features and select the appropriate interpreter for newly joining agents without needing their configurations. Experiments on simulated and real datasets show that PEARL improves average precision by 8.2% over random selection, runs in 1.67 ms, and reduces communication cost by up to 34.7× while outperforming state‑of‑the‑art offline methods by 5.6% AP.
By Armin Maleki, Hayder Radha
FedCKA introduces a Centered Kernel Alignment (CKA)-based method for federated 3D perception that dynamically balances personalization and globalization. By computing layer-wise feature similarities between local client models and a global consensus model, FedCKA generates client‑specific aggregation masks to selectively share representation‑consistent layers. Experiments on a unified multi‑domain nuScenes benchmark demonstrate that FedCKA surpasses established federated baselines, improving average NDS by 7 percentage points.
By Jolle Verhoog, Ali Burak \"Unal, Holger Caesar
arXiv:2609.00951v1 Announce Type: new
Abstract: Collaborative perception shares information among multiple agents to obtain a comprehensive scene representation, enhancing the perceptual capability o...
By Jiuwu Hao, Ziyi Ni, Liguo Sun, Yuting Wan, Yueyang Wu, Ti Xiang, Haolin Song, Pin Lv
arXiv:2603.19308v2 Announce Type: replace-cross
Abstract: In autonomous driving, multi-agent collaborative perception enhances sensing capabilities by enabling agents to share perceptual data. A key...
By Wentao Wang, Haoran Xu, Guang Tan
Collaborative perception shares information among multiple agents to obtain a comprehensive scene representation, enhancing the perceptual capability of individual agents. However, most existing metho...
arXiv:2503.12232v3 Announce Type: replace
Abstract: Aiming to match pedestrian images captured under varying lighting conditions, visible-infrared person re-identification (VI-ReID) has drawn intensi...
By Yan Jiang, Hao Yu, Xu Cheng, Haoyu Chen, Zhaodong Sun, Guoying Zhao