核心概念
Maximizing mutual information between individual and collaborative views enhances multi-agent perception.
摘要
The paper explores the importance of collaborative views in multi-agent perception, focusing on maximizing mutual information. It introduces CMiMC, a framework that preserves discriminative information while enhancing collaborative views' efficacy. By defining multi-view mutual information (MVMI), CMiMC improves average precision by 3.08% and 4.44% at IoU thresholds of 0.5 and 0.7, respectively. The method reduces communication volume significantly while maintaining performance comparable to the state-of-the-art.
統計資料
CMiMC improves SOTA average precision by 3.08% and 4.44% at IoU thresholds of 0.5 and 0.7.
CMiMC can reduce communication volume to 1/32 while achieving performance comparable to SOTA.
引述
"CMiMC defines multi-view mutual information that properly measures the global and local dependencies between a collaborative view and multiple individual views."
"CMiMC outperforms state-of-the-art benchmarks in terms of average precision and performance-bandwidth trade-offs."