打破数据孤岛：面向独立私有来源的多智能体感知跨域学习
Breaking Data Silos: Cross-Domain Learning for Multi-Agent Perception from Independent Private Sources
AI总结：
本文针对多智能体感知中因各公司私有训练数据分布差异导致的数据孤岛和性能下降问题，提出特征分布感知聚合（FDA）跨域学习框架，通过特征补偿与分布一致性模块缩小智能体间特征分布差距，并在OPV2V和V2XSet上验证了其在三维目标检测中的有效性。
摘要：
多智能体感知系统中的不同智能体可能来自不同公司。每家公司可能使用相同的经典神经网络架构编码器进行特征提取。然而，用于训练各智能体的数据来源在各公司内部是独立且私有的，导致多智能体感知系统中训练不同智能体所使用的不同私有数据之间存在分布差距。由上述分布差距造成的数据孤岛可能导致多智能体感知性能显著下降。本文深入考察了分布差距对现有多智能体感知系统的影响。为打破数据孤岛，我们提出了特征分布感知聚合（FDA）框架，用于跨域学习，以缓解多智能体感知中的上述分布差距。FDA包含两个关键组件：可学习特征补偿模块和分布感知统计一致性模块，二者均旨在增强中间特征，以最小化多智能体特征之间的分布差距。在公开的OPV2V和V2XSet数据集上的大量实验证明了FDA在基于点云的三维目标检测中的有效性，表明它是现有多智能体感知系统的重要增强手段。
英文摘要：
The diverse agents in multi-agent perception systems may be from different companies. Each company might use the identical classic neural network architecture based encoder for feature extraction. However, the data source to train the various agents is independent and private in each company, leading to the Distribution Gap of different private data for training distinct agents in multi-agent perception system. The data silos by the above Distribution Gap could result in a significant performance decline in multi-agent perception. In this paper, we thoroughly examine the impact of the distribution gap on existing multi-agent perception systems. To break the data silos, we introduce the Feature Distribution-aware Aggregation (FDA) framework for cross-domain learning to mitigate the above Distribution Gap in multi-agent perception. FDA comprises two key components: Learnable Feature Compensation Module and Distribution-aware Statistical Consistency Module, both aimed at enhancing intermediate features to minimize the distribution gap among multi-agent features. Intensive experiments on the public OPV2V and V2XSet datasets underscore FDA's effectiveness in point cloud-based 3D object detection, presenting it as an invaluable augmentation to existing multi-agent perception systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
