← 研踪 PaperTrailarXiv:2603.23975 · v2
HyDRA: Hybrid Domain-Aware Robust Architecture for Heterogeneous Collaborative Perception
首次提交 2026-03-25 14:15
版本更新 2026-11-06 14:51
雷达观测 2026-11-09 11:19
摘要
Collaborative perception assumes that participating agents share compatible feature representations, yet this assumption often fails in practice. Beyond explicit differences in backbone architecture or sensor modality, independently trained models can exhibit latent domain shifts and cause naive feature fusion to contaminate rather than complement each agent's representation. To address this, we propose HyDRA (Hybrid Domain-Aware Robust Architecture), a training-free framework that uses a lightweight domain classifier to identify whether each agent shares a compatible feature domain, and adaptively select the fusion strategy. Homogeneous agents are combined through intermediate fusion to maximize information gain, whereas heterogeneous agents are routed to late fusion to avoid contamination. To further mitigate localization errors inherent in late fusion, HyDRA incorporates anchor-guided pose graph optimization. Extensive experiments show that HyDRA matches state-of-the-art methods under architecture heterogeneity, and improves overall AP@0.7 over late fusion by nearly 25% under latent domain heterogeneity.
作者备注：Accepted to WACV 2027. 9 pages main paper + 5 pages supplementary material, 6 figures
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v2 · 2026-11-06 14:51 · 最新观测版本
HyDRA: Hybrid Domain-Aware Robust Architecture for Heterogeneous Collaborative Perception
Collaborative perception assumes that participating agents share compatible feature representations, yet this assumption often fails in practice. Beyond explicit differences in backbone architecture or sensor modality, independently trained models can exhibit latent domain shifts and cause naive feature fusion to contaminate rather than complement each agent's representation. To address this, we propose HyDRA (Hybrid Domain-Aware Robust Architecture), a training-free framework that uses a lightweight domain classifier to identify whether each agent shares a compatible feature domain, and adaptively select the fusion strategy. Homogeneous agents are combined through intermediate fusion to maximize information gain, whereas heterogeneous agents are routed to late fusion to avoid contamination. To further mitigate localization errors inherent in late fusion, HyDRA incorporates anchor-guided pose graph optimization. Extensive experiments show that HyDRA matches state-of-the-art methods under architecture heterogeneity, and improves overall AP@0.7 over late fusion by nearly 25% under latent domain heterogeneity.
查看此版本解读 →查看此版本原文 ↗