arXiv Computer Vision
Evaluating 2D and 3D-Aware Vision Foundation Models for Vehicle Attribute Recognition
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A Benchmark for Vehicle Attribute Classification in Cross-Domain Surveillance Scenarios
arXiv:2609.01584v1 Announce Type: new Abstract: Vehicle attribute analysis is a key component of Intelligent Transportation Systems (ITS), supporting applications such as vehicle identification, traf...
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Real-Time Source-Free Object Detection
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OpenBox: Annotate Any Bounding Boxes in 3D
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FedCKA: Representation-Guided Layer Personalization for Federated 3D Perception Across Driving Domains
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arXiv Computer Vision
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arXiv AI
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Rethinking Pre-Training and Augmentation for Zero-Shot Cross-City Object Detection
The paper proposes a modular training pipeline for zero‑shot cross‑city object detection that combines a multi‑dataset pre‑training strategy with class‑agnostic objectness distillation and a domain‑resilient augmentation stream featuring a Grayworld transformation. Applied to the RF‑DETR detector, the approach reduces cross‑city distribution gaps while using only 16 GB GPU memory, achieving a 24.29‑point mAP improvement and 1st place on the AI City Challenge Track 6 leaderboard. The authors provide code and data at the referenced GitHub repository.