arXiv Computer Vision
Contour-Guided Spectral Routing for Robust Real-Time Pedestrian Detection
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arXiv Computer Vision
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CLFTv2: Efficient Camera-LiDAR Fusion for Semantic Segmentation via Hierarchical Feature Pyramids
arXiv:2609.09881v1 Announce Type: new Abstract: Semantic segmentation for autonomous driving requires reliable detection of vulnerable road users (VRUs) despite heavy class imbalance. We introduce CL...
arXiv AI
Sep 1
Making the Discrete Continuous: Synthetic RAW Augmentations for Fine-Grained Evaluation of Person Detection Performance in Low Light
arXiv:2605.22455v2 Announce Type: replace-cross Abstract: Real-world deployment of AI vision models is both fueled and limited by the data available for training and testing. Real datasets are sparse...
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.
arXiv AI
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Pedestrian Crossing Intent Classification From Event-Based Vision Using Convolutional Spiking Neural Networks With Temporal Augmentation
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arXiv AI
Aug 25
Multi-Context Fusion Transformer for Pedestrian Crossing Intention Prediction in Urban Environments
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arXiv AI
Jun 9
ATN3D: Density-Aware LiDAR-Radar Early 3D Object Detection Under Extreme Sparsity
arXiv:2606. 09634v1 Announce Type: cross Abstract: 3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications.