RailGen: Improving Railway Intrusion Detection via Agent-Guided Small-Scale Foreign Object Generation

arXiv:2608.30727 · cs.CV, cs.AI · Submitted 2026-08-31 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-08-31

Updated: 2026-08-31

Code: https://github.com/black-forest-labs/flux

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

Terminology

• Consistency — 17× in this paper · explained in 3 episodes
• Flow Matching — 11× in this paper · explained in 8 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Adaptation — 6× in this paper · explained in 1 episode
• Ablation — 5× in this paper · explained in 1 episode
• Railway Foreign Object Detection — 5× in this paper
• Alignment — 4× in this paper · explained in 1 episode
• Diffusion Model — 4× in this paper · explained in 8 episodes
• Diffusion Models — 4× in this paper · explained in 6 episodes
• Physical Constraints — 4× in this paper · explained in 7 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Accuracy — 3× in this paper · explained in 1 episode
• Aggregation — 3× in this paper · explained in 1 episode
• Fidelity — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Semantic Alignment — 3× in this paper · explained in 3 episodes
• Structure-Aware Physical Conditional Injection — 3× in this paper
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• LLM-as-a-Judge — 2× in this paper · explained in 3 episodes
• Low-Rank Adaptation (LoRA) — 2× in this paper · explained in 9 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Catenary Electrical System Dataset — 1× in this paper
• Computational overhead — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foreign Object Visual Quality — 1× in this paper
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Hybrid System — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Scene Realism — 1× in this paper
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Task Planning — 1× in this paper · explained in 2 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• YOLOv12: Attention-Centric Real-Time Object Detectors
• YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception
• Auto-Encoding Variational Bayes
• Generative Adversarial Networks
• Flow Matching for Generative Modeling
• FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
• Gemma 3 Technical Report
• Learning Transferable Visual Models From Natural Language Supervision

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