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arXiv Computer Vision

HeteroPROMPT: A Real-time and Privacy-Preserving Heterogeneous Collaborative Perception Framework

HeteroPROMPT is a real‑time, privacy‑preserving framework for heterogeneous collaborative perception in autonomous systems. It aligns features from diverse sensors and models into a unified ego‑centric space using modular prompts and lightweight tuning, while keeping encoders and fusion stacks frozen. The system employs a metadata‑free autoencoder for modality classification and routing, achieving higher average precision on OPV2V‑H and V2XSet datasets with far fewer trainable parameters.

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arXiv Machine Learning
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PEARL: A Lightweight Prompt-based Feature Interpreter Framework for Real-Time, Anonymous, and Heterogeneous Collaborative Perception

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.

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arXiv Computer Vision
Oct 2

FedCKA: Representation-Guided Layer Personalization for Federated 3D Perception Across Driving Domains

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
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arXiv Machine Learning
Jul 28

SimBEV2X: A Large-Scale Dataset and Data Generation Tool for Multi-Task Vehicle-to-Everything Cooperative Perception

arXiv:2607. 23910v1 Announce Type: cross Abstract: Cooperative perception through vehicle-to-everything (V2X) communication can overcome the inherent physical limitations of individual autonomous vehicles, such as occlusions and limited sensor range.

By Goodarz Mehr, Sepideh Gohari, Montasir Abbas, Azim Eskandarian
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arXiv Machine Learning
Sep 17

Learning from Distributed Eyes: Leveraging Collaborative Perception for Automated Model Adaptation

The paper introduces LDE, a framework that uses collaborative perception (CP) to generate high‑quality pseudo‑labels for unsupervised model adaptation in autonomous driving. It tackles communication limits, field‑of‑view mismatches, and label unreliability through selective feature sharing, FoV filtering, and curriculum learning. Experiments on 3D object detection show LDE surpasses pre‑trained models and existing adaptation methods.

By Yanan Ma, Yihang Tao, Zhengru Fang, Zihan Fang, Yiqin Deng, Xianhao Chen, Yuguang Fang
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arXiv AI
Aug 28

Beyond Classification: Task-Dependent Learnability under Privacy-Motivated Image Transformations

The paper argues that evaluating privacy‑enhancing technologies (PETs) solely through image classification is insufficient because classification remains robust to many geometric and local perturbations. It proposes a compute‑aware multi‑task protocol that uses lightweight proxy tasks to assess PETs across various transformations, revealing that PETs with similar classification accuracy can perform very differently on other vision tasks. The study demonstrates the necessity of broader evaluation metrics beyond classification to truly gauge PET effectiveness.

By Leon Ranke, Wolfgang H\"ubner, Ronny Hug, Michael Arens, J\"urgen Beyerer
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arXiv AI
Sep 4

CauseCollab: Causal Unified and Modality-Agnostic Network for Heterogeneous Collaborative Perception

CauseCollab is a causal unified and modality‑agnostic network designed to improve collaborative perception across heterogeneous sensor modalities. It disentangles semantic factors from modality‑specific confounders using causal metric learning and employs a context‑guided Unified Converter to maintain cross‑modal semantic consistency. The approach requires only minimal adapter training when adding new modalities and achieves state‑of‑the‑art results on the OPV2V and DAIR‑V2X datasets, especially in scenarios with large modality gaps.

By Weize Li, Yang Li, Quan Yuan, Xiaoyuan Fu, Guiyang Luo, Jinglin Li
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