# Data Generation Scheme for Thermal Modality with Edge-Guided Adversarial Conditional Diffusion Model

arXiv: https://arxiv.org/abs/2408.03748 · HTML: https://mlsanity.com/paper/2408.03748/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 30 of its 53 references that are in the map.
- First posted on arXiv 7 Aug 2024.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [VarifocalNet: An IoU-aware Dense Object Detector](https://mlsanity.com/paper/2008.13367/) (2020)
- [LLVIP: A Visible-infrared Paired Dataset for Low-light Vision](https://mlsanity.com/paper/2108.10831/) (2021)
- [BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models](https://mlsanity.com/paper/2205.07680/) (2022)
- [Diffusion-GAN: Training GANs with Diffusion](https://mlsanity.com/paper/2206.02262/) (2022)
- [U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation](https://mlsanity.com/paper/1907.10830/) (2019)
- [EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations](https://mlsanity.com/paper/2207.06635/) (2022)
- [Person Re-identification in the Wild](https://mlsanity.com/paper/1604.02531/) (2016)
- [High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network](https://mlsanity.com/paper/2105.09188/) (2021)
- [Learning Deep Multi-Level Similarity for Thermal Infrared Object Tracking](https://mlsanity.com/paper/1906.03568/) (2019)
- [Contributions of Shape, Texture, and Color in Visual Recognition](https://mlsanity.com/paper/2207.09510/) (2022)
- [Pedestrian Detection in Thermal Images using Saliency Maps](https://mlsanity.com/paper/1904.06859/) (2019)
- [Unpaired Image Translation via Vector Symbolic Architectures](https://mlsanity.com/paper/2209.02686/) (2022)
- [TCDesc: Learning Topology Consistent Descriptors for Image Matching](https://mlsanity.com/paper/2009.07036/) (2020)
- [Robust pedestrian detection in thermal imagery using synthesized images](https://mlsanity.com/paper/2102.02005/) (2021)
- [Unsupervised Image-generation Enhanced Adaptation for Object Detection in Thermal images](https://mlsanity.com/paper/2002.06770/) (2020)
- [Exploring Thermal Images for Object Detection in Underexposure Regions for Autonomous Driving](https://mlsanity.com/paper/2006.00821/) (2020)
- [Partially fake it till you make it: mixing real and fake thermal images for improved object detection](https://mlsanity.com/paper/2106.13603/) (2021)

## Built on by (most cited first, 1 of 1)

- [ThermalGen: Style-Disentangled Flow-Based Generative Models for RGB-to-Thermal Image Translation](https://mlsanity.com/paper/2509.24878/) (2025)

## Abstract (arXiv)

In challenging low light and adverse weather conditions,thermal vision algorithms,especially object detection,have exhibited remarkable potential,contrasting with the frequent struggles encountered by visible vision algorithms. Nevertheless,the efficacy of thermal vision algorithms driven by deep learning models remains constrained by the paucity of available training data samples. To this end,this paper introduces a novel approach termed the edge guided conditional diffusion model. This framework aims to produce meticulously aligned pseudo thermal images at the pixel level,leveraging edge information extracted from visible images. By utilizing edges as contextual cues from the visible domain,the diffusion model achieves meticulous control over the delineation of objects within the generated images. To alleviate the impacts of those visible-specific edge information that should not appear in the thermal domain,a two-stage modality adversarial training strategy is proposed to filter them out from the generated images by differentiating the visible and thermal modality. Extensive experiments on LLVIP demonstrate ECDM s superiority over existing state-of-the-art approaches in terms of image generation quality.