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arXiv AI By Yu Wang, Shaohua Wang, Yicheng Li, Kecheng Sun

Agent-Wise Test-Time Normalization for Camera-Based Cooperative Perception with Corrupted Collaborators

Read the original on arXiv AI →

The paper introduces AgentNorm, an agent‑wise test‑time normalization technique for camera‑based cooperative perception. Unlike pooled batch‑normalization, AgentNorm estimates normalization statistics separately for each collaborator and anchors the ego vehicle to its own statistics, preventing degradation when a collaborator’s cameras are corrupted by fog, motion blur, or exposure errors. Experiments on OPV2V and DAIR‑V2X‑C datasets show significant AP improvements under severe fog and motion blur, with minimal added latency.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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