mlsanityData to 14 Oct 2026

ContRail: A Framework for Realistic Railway Image Synthesis using ControlNet

arXiv 2412.06742 ↗First posted 9 Dec 2024cs.CV cs.AIcited by 0

On the map: Diffusion models → Image generation, text-to-image and editing

Abstract (arXiv)

Deep Learning became an ubiquitous paradigm due to its extraordinary effectiveness and applicability in numerous domains. However, the approach suffers from the high demand of data required to achieve the potential of this type of model. An ever-increasing sub-field of Artificial Intelligence, Image Synthesis, aims to address this limitation through the design of intelligent models capable of creating original and realistic images, endeavour which could drastically reduce the need for real data. The Stable Diffusion generation paradigm recently propelled state-of-the-art approaches to exceed all previous benchmarks. In this work, we propose the ContRail framework based on the novel Stable Diffusion model ControlNet, which we empower through a multi-modal conditioning method. We experiment with the task of synthetic railway image generation, where we improve the performance in rail-specific tasks, such as rail semantic segmentation by enriching the dataset with realistic synthetic images.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 20 of its 34 references that are in the map. First posted on arXiv 9 Dec 2024.

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20 of 34 references.

Closest ancestors · 5

Same direction, newest first

  1. 2023SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing18 Dec
  2. 2023Analyzing Bias in Diffusion-based Face Generation Models10 May
  3. 2023T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models16 Feb
  4. 2023Adding Conditional Control to Text-to-Image Diffusion Models10 Feb
  5. 2022Compositional Visual Generation with Composable Diffusion Models3 Jun

Other references · 15

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  4. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
  5. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  6. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,152
  7. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
  8. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,299
  9. 2016HyperNetworkscited by 1,027
  10. 2015Holistically-Nested Edge Detectioncited by 592
  11. 2018GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classificationcited by 242
  12. 2021ZeroCap: Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmeticcited by 150
3 more
  1. 2021Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspectivecited by 116
  2. 2023Deep Learning Approaches for Data Augmentation in Medical Imaging: A Reviewcited by 48
  3. 2022Can segmentation models be trained with fully synthetically generated data?cited by 37

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  1. 2024From Noise to Nuance: Advances in Deep Generative Image Models
  2. 2024Label-free Neural Semantic Image Synthesis
  3. 2023Modulating Pretrained Diffusion Models for Multimodal Image Synthesis
  4. 2023State of the Art on Diffusion Models for Visual Computing
  5. 2023Efficient-3DiM: Learning a Generalizable Single-image Novel-view Synthesizer in One Day
  6. 2025Flow Matching for Medical Image Synthesis: Bridging the Gap Between Speed and Quality
  7. 2025Bounding Box-Guided Diffusion for Synthesizing Industrial Images and Segmentation Map
  8. 2025DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation