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arXiv Machine Learning

Classification Drives Geographic Bias in Street Scene Segmentation

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Hugging Face Trending Papers
Jul 29

Object Detection for Autonomous Driving in Chinese Rural Scenes: An Experimental Study on Real-Synthetic Data Mixing and Model Evaluation

Currently, autonomous driving object detection models face significant data scarcity and generalization challenges when navigating complex Chinese rural traffic scenarios. To address these limitations, we propose a novel real-synthetic mixed object detection dataset tailored specifically for Chinese rural roads and systematically evaluate the performance of 13 mainstream detectors under different real-to-synthetic data ratios, thereby providing empirical evidence for model selection and data strategy design in rural autonomous driving scenarios.

agentscomputer-visionrobotics
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arXiv Machine Learning
Jun 4

An Open-Source Two-Stage Computer Vision Pipeline for Fine-Grained Vehicle Classification using Vision Transformers

arXiv:2606. 05149v1 Announce Type: cross Abstract: Vehicle body type is a significant determinant of cyclist injury severity in overtaking crashes, yet automated tools for classifying vehicles into injury-risk-relevant categories from naturalistic roadway video do not exist in the open literature.

By Gandhimathi Padmanaban, Fred Feng
llmscomputer-visionfine-tuningbenchmarkssafety
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Hugging Face Trending Papers
Jul 30

Scaling Vision-Language Models Is Not Enough to Mitigate Bias

Vision-Language Models (VLMs) such as CLIP are now foundational to multimodal systems, yet their robustness to spurious correlations remains poorly understood at scale. We present the first large-scale empirical study of 194 publicly available VLMs, including 16 model families, covering a wide range of model sizes, 24 training datasets, and three evaluation benchmarks, namely ImageNet (overall performance), CelebA (typical single-attribute bias), and UrbanCars (complex multi-attribute biases).

llmsmultimodalbenchmarkssafety
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arXiv Computer Vision
Aug 27

OpenCVL: An Open, Diverse, and Large-Scale Dataset for Fine-Grained Cross-View Localization

OpenCVL is a large, open dataset for fine-grained cross-view localization, comprising 617,388 ground‑aerial image pairs from 41 European cities. It blends high‑end sensor data with diverse in‑the‑wild images and includes a curation framework to correct pose annotations, enabling reliable evaluation. The dataset also offers cross‑area and snowy test sets to probe generalization, and experiments show that adding noisy in‑the‑wild data improves model performance on clean tests.

By Zimin Xia, Mubariz Zaffar, Junsheng Fu, Alexandre Alahi, Julian F. P. Kooij
benchmarks
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arXiv AI
Sep 1

Background-Free Objectness Learning for Class-Agnostic Detection

Background-Free Objectness Learning (B-FOR) is a dense, class‑agnostic detection framework that learns objectness without treating unlabeled regions as background. It predicts multi‑scale object‑center and scale fields, using spatially structured soft targets to supervise only reliable annotated areas and introduces displacement‑aware scale fields to model object extent. Experiments on PASCAL VOC, MS‑COCO, and Open Images show B‑FOR improves recall by over +10 AR points compared to prior class‑agnostic baselines, with ablation studies confirming the importance of localized supervision and displacement‑aware scaling.

By Dania Batool, Liliana Lo Presti, Marco La Cascia, Filippo Vella
benchmarkssafety
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