mlsanityData to 14 Oct 2026

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

arXiv 2605.00405 ↗First posted 1 May 2026cs.CVcited by 1

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Abstract (arXiv)

Most existing heterogeneous cooperative perception methods depend on prior preparation like offline joint training or tailored collaborator-model adaptation. Such preprocessing is, however, generally impractical in real scenarios, as agents are usually independently trained by different developers and meet occasionally online. This work investigates preparation-free heterogeneous cooperative perception, where agents use independently trained single-agent detectors without any pre-deployment coordination. We find direct cross-agent fusion under this setting greatly underperforms ego-only perception. We present BOLT, a lightweight plug-and-play module that adapts neighboring features online via ego-as-teacher distillation, requiring only ego predictions without ground-truth labels. BOLT leverages high-confidence ego perception features to guide cross-agent feature-domain alignment, while enabling neighbors to contribute features in the ego's low-confidence regions. With only 0.9M trainable parameters, BOLT improves AP@50 by up to 32.3 points over vanilla unadapted fusion in the preparation-free setting. It consistently outperforms ego-only results on DAIR-V2X and OPV2V, across different encoder pairs and fusion strategies. Code: https://github.com/sidiangongyuan/BOLT.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 38 of its 48 references that are in the map. First posted on arXiv 1 May 2026.

Builds on

38 of 48 references.

References · 38

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26 more
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Citing papers per year: 2026: 1.

1 paper.

  1. 2026CoVLM-Bench: A Real-World Benchmark for Cooperative Driving Question Answering and Planningcited by 0