arXiv Computer Vision
GIFT: Representation Geometry Matters for Single-Domain Generalized Object Detection
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arXiv Computer Vision
Oct 2
Domain generalization and synthetic data in object detection: the enabler, the probe, and the gap
The paper reviews domain generalization (DG) for object detection, highlighting its scarcity and unique challenges such as localization and multi-scale representation. It examines synthetic data from three angles: as an enabler that diversifies and aligns training data, as a probe that allows controlled experiments to uncover failure modes, and as a source of a synthetic‑to‑real gap that hampers deployment on real imagery. The authors conclude that future DG research must develop representation‑aware methods that explicitly address both localization and classification under domain shift.
arXiv Computer Vision
Sep 28
VFM-UDA++: Improving Network Architectures and Data Strategies for Unsupervised Domain Adaptive Semantic Segmentation
arXiv:2503.10685v3 Announce Type: replace Abstract: Unsupervised Domain Adaptation (UDA) enables strong generalization from a labeled source domain to an unlabeled target domain, often with limited d...
arXiv AI
Aug 26
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
Jul 3
AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models
arXiv:2607. 02269v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated immense promise in Spatio-Temporal Video Grounding (STVG).
arXiv Computer Vision
Oct 2
Weather-Aware Domain Adaptation for Street-View Weather Recognition
arXiv:2610.02000v1 Announce Type: new Abstract: Adverse conditions such as rain, snow, fog, and dust remain challenging for camera-based perception in autonomous driving. We study multi-class weather...
arXiv Computer Vision
Sep 28
Exploring the Benefits of Vision Foundation Models for Unsupervised Domain Adaptation
arXiv:2406.09896v3 Announce Type: replace Abstract: Achieving robust generalization across diverse data domains remains a significant challenge in computer vision. This challenge is important in safe...