论文标题
模型不合时宜的多机构感知框架
Model-Agnostic Multi-Agent Perception Framework
论文作者
论文摘要
现有的多代理感知系统假设每个代理都使用具有相同参数和体系结构的相同模型。由于置信度评分的不匹配，因此可以通过不同的感知模型来降低性能。在这项工作中，我们提出了一个模型不合时宜的多代理感知框架，以减少由模型差异造成的负面影响，而无需共享模型信息。具体而言，我们提出了一个可以消除预测置信度得分偏置的置信校准器。每个代理商在标准的公共数据库中独立执行此类校准，以保护知识产权。我们还提出了一种相应的边界框聚合算法，该算法考虑了相邻框的置信度得分和空间协议。我们的实验阐明了不同试剂之间模型校准的必要性，结果表明，提出的框架改善了异质剂的基线3D对象检测性能。
Existing multi-agent perception systems assume that every agent utilizes the same model with identical parameters and architecture. The performance can be degraded with different perception models due to the mismatch in their confidence scores. In this work, we propose a model-agnostic multi-agent perception framework to reduce the negative effect caused by the model discrepancies without sharing the model information. Specifically, we propose a confidence calibrator that can eliminate the prediction confidence score bias. Each agent performs such calibration independently on a standard public database to protect intellectual property. We also propose a corresponding bounding box aggregation algorithm that considers the confidence scores and the spatial agreement of neighboring boxes. Our experiments shed light on the necessity of model calibration across different agents, and the results show that the proposed framework improves the baseline 3D object detection performance of heterogeneous agents.
