# ROMA: Cross-Domain Region Similarity Matching for Unpaired Nighttime Infrared to Daytime Visible Video Translation

arXiv: https://arxiv.org/abs/2204.12367 · HTML: https://mlsanity.com/paper/2204.12367/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 45 references that are in the map.
- First posted on arXiv 26 Apr 2022.

## Builds on (most cited first, 28 of 28)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Video-to-Video Synthesis](https://mlsanity.com/paper/1808.06601/) (2018)
- [Photographic Image Synthesis with Cascaded Refinement Networks](https://mlsanity.com/paper/1707.09405/) (2017)
- [Generating Images with Perceptual Similarity Metrics based on Deep Networks](https://mlsanity.com/paper/1602.02644/) (2016)
- [Few-shot Video-to-Video Synthesis](https://mlsanity.com/paper/1910.12713/) (2019)
- [The Contextual Loss for Image Transformation with Non-Aligned Data](https://mlsanity.com/paper/1803.02077/) (2018)
- [Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping](https://mlsanity.com/paper/1809.05852/) (2018)
- [One-Sided Unsupervised Domain Mapping](https://mlsanity.com/paper/1706.00826/) (2017)
- [Recycle-GAN: Unsupervised Video Retargeting](https://mlsanity.com/paper/1808.05174/) (2018)
- [The Spatially-Correlative Loss for Various Image Translation Tasks](https://mlsanity.com/paper/2104.00854/) (2021)
- [Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks](https://mlsanity.com/paper/2203.01532/) (2022)
- [Mocycle-GAN: Unpaired Video-to-Video Translation](https://mlsanity.com/paper/1908.09514/) (2019)
- [VIFB: A Visible and Infrared Image Fusion Benchmark](https://mlsanity.com/paper/2002.03322/) (2020)
- [PCSGAN: Perceptual Cyclic-Synthesized Generative Adversarial Networks for Thermal and NIR to Visible Image Transformation](https://mlsanity.com/paper/2002.07082/) (2020)
- [IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation](https://mlsanity.com/paper/1806.09565/) (2018)
- [I2V-GAN: Unpaired Infrared-to-Visible Video Translation](https://mlsanity.com/paper/2108.00913/) (2021)
- [Unsupervised Video-to-Video Translation](https://mlsanity.com/paper/1806.03698/) (2018)
- [Infrared Colorization Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1604.02245/) (2016)
- [Infrared and visible image fusion using Latent Low-Rank Representation](https://mlsanity.com/paper/1804.08992/) (2018)

## Built on by (most cited first, 5 of 5)

- [UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation](https://mlsanity.com/paper/2502.02257/) (2025)
- [CM-Diff: A Single Generative Network for Bidirectional Cross-Modality Translation Diffusion Model Between Infrared and Visible Images](https://mlsanity.com/paper/2503.09514/) (2025)
- [Grab-3D: Detecting AI-Generated Videos from 3D Geometric Temporal Consistency](https://mlsanity.com/paper/2512.13665/) (2025)
- [WAIT: Feature Warping for Animation to Illustration video Translation using GANs](https://mlsanity.com/paper/2310.04901/) (2023)
- [TC-PDM: Temporally Consistent Patch Diffusion Models for Infrared-to-Visible Video Translation](https://mlsanity.com/paper/2408.14227/) (2024)

## Abstract (arXiv)

Infrared cameras are often utilized to enhance the night vision since the visible light cameras exhibit inferior efficacy without sufficient illumination. However, infrared data possesses inadequate color contrast and representation ability attributed to its intrinsic heat-related imaging principle. This makes it arduous to capture and analyze information for human beings, meanwhile hindering its application. Although, the domain gaps between unpaired nighttime infrared and daytime visible videos are even huger than paired ones that captured at the same time, establishing an effective translation mapping will greatly contribute to various fields. In this case, the structural knowledge within nighttime infrared videos and semantic information contained in the translated daytime visible pairs could be utilized simultaneously. To this end, we propose a tailored framework ROMA that couples with our introduced cRoss-domain regiOn siMilarity mAtching technique for bridging the huge gaps. To be specific, ROMA could efficiently translate the unpaired nighttime infrared videos into fine-grained daytime visible ones, meanwhile maintain the spatiotemporal consistency via matching the cross-domain region similarity. Furthermore, we design a multiscale region-wise discriminator to distinguish the details from synthesized visible results and real references. Extensive experiments and evaluations for specific applications indicate ROMA outperforms the state-of-the-art methods. Moreover, we provide a new and challenging dataset encouraging further research for unpaired nighttime infrared and daytime visible video translation, named InfraredCity. In particular, it consists of 9 long video clips including City, Highway and Monitor scenarios. All clips could be split into 603,142 frames in total, which are 20 times larger than the recently released daytime infrared-to-visible dataset IRVI.