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arXiv Computer Vision By Kang Yang, Tianci Bu, Peng Wang, Deying Li, Yongcai Wang

BOLT: Online Lightweight Adaptation for Preparation-Free Heterogeneous Cooperative Perception

Read the original on arXiv Computer Vision →

BOLT is a lightweight plug‑and‑play module that enables preparation‑free heterogeneous cooperative perception by adapting neighboring features online through ego‑as‑teacher distillation. It requires only ego predictions, no ground‑truth labels, and uses high‑confidence ego features to align cross‑agent feature domains while allowing neighbors to contribute in low‑confidence regions. With just 0.9 M trainable parameters, BOLT boosts AP@50 by up to 32.3 points over unadapted fusion and consistently outperforms ego‑only results on DAIR‑V2X and OPV2V.

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 Computer Vision.

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