Rethinking Pre-Training and Augmentation for Zero-Shot Cross-City Object Detection

arXiv:2608.24154 · cs.CV, cs.AI · Submitted 2026-08-25 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-08-25

Updated: 2026-08-25

Terminology

• Transformer — 15× in this paper · explained in 2 episodes
• Benchmark — 12× in this paper · explained in 2 episodes
• Domain Shift — 11× in this paper · explained in 4 episodes
• Adaptation — 10× in this paper · explained in 1 episode
• Fine-Tuning — 8× in this paper · explained in 5 episodes
• Distillation — 7× in this paper · explained in 2 episodes
• Calibration — 6× in this paper · explained in 7 episodes
• Conference on Computer Vision and Pattern Recognition — 6× in this paper
• Benchmarking — 5× in this paper · explained in 3 episodes
• Domain Adaptation — 5× in this paper · explained in 5 episodes
• European Conference on Computer Vision — 5× in this paper
• Generalization — 5× in this paper · explained in 13 episodes
• CVF International Conference on Computer Vision — 4× in this paper
• Alignment — 3× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• Data Augmentation — 3× in this paper · explained in 7 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Pseudo-labels — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• Unsupervised Domain Adaptation — 2× in this paper
• Vision-Language Model — 2× in this paper
• Ablation study — 1× in this paper · explained in 1 episode
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Hafnia Training-as-a-Service — 1× in this paper
• International Conference on Machine Learning — 1× in this paper
• Latent Representation — 1× in this paper · explained in 2 episodes
• Over-smoothing — 1× in this paper · explained in 4 episodes
• Pseudo-labeling — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Vision-Language Model (VLM) — 1× in this paper · explained in 3 episodes

Sources

• Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
• MOT20: A benchmark for multi object tracking in crowded scenes

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models