Semantically-Guided Domain Randomization for Industrial Object Detection in Low-Image-Budget Regimes

arXiv:2609.26505 · cs.CV · Submitted 2026-09-22 · Read on arXiv

cs.CV

Submitted: 2026-09-22

Updated: 2026-09-22

Code: https://github.com/ultralytics/ultralytics

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adaptation — 10× in this paper · explained in 1 episode
• Benchmark — 8× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Grounding — 7× in this paper · explained in 3 episodes
• Diffusion Model — 6× in this paper · explained in 8 episodes
• Diffusion Models — 6× in this paper · explained in 6 episodes
• Domain Adaptation — 5× in this paper · explained in 5 episodes
• Computer Vision and Pattern Recognition — 4× in this paper
• Active Learning — 3× in this paper · explained in 6 episodes
• Data Augmentation — 3× in this paper · explained in 7 episodes
• Synthetic Active Learning — 3× in this paper
• Synthetic Data Generation — 3× in this paper · explained in 5 episodes
• Vision-Language Model — 3× in this paper
• Accuracy — 2× in this paper · explained in 1 episode
• International Conference on Robotics and Automation — 2× in this paper
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Segmentation Masks — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Sim-to-Real Gap — 1× in this paper · explained in 6 episodes
• Sim-to-Real Transfer — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Vision-Language Model (VLM) — 1× in this paper · explained in 3 episodes

Sources

• SynthRender and I-AsSET: Open-Source Framework and Dataset for Bidirectional Sim-Real Transfer in Industrial Object Perception
• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
• SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
• IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
• Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

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