# Generative Adversarial Networks for Video-to-Video Domain Adaptation

arXiv: https://arxiv.org/abs/2004.08058 · HTML: https://mlsanity.com/paper/2004.08058/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2020.

## Built on by (most cited first, 11 of 11)

- [Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives](https://mlsanity.com/paper/2208.07422/) (2022)
- [Deep Gait Recognition: A Survey](https://mlsanity.com/paper/2102.09546/) (2021)
- [Domain Adaptation Meets Zero-Shot Learning: An Annotation-Efficient Approach to Multi-Modality Medical Image Segmentation](https://mlsanity.com/paper/2203.10332/) (2022)
- [Unsupervised Domain Adaptation for Video Semantic Segmentation](https://mlsanity.com/paper/2107.11052/) (2021)
- [AceVFI: A Comprehensive Survey of Advances in Video Frame Interpolation](https://mlsanity.com/paper/2506.01061/) (2025)
- [Unsupervised Cardiac Video Translation Via Motion Feature Guided Diffusion Model](https://mlsanity.com/paper/2507.02003/) (2025)
- [Mutual-GAN: Towards Unsupervised Cross-Weather Adaptation with Mutual Information Constraint](https://mlsanity.com/paper/2106.16000/) (2021)
- [VoloGAN: Adversarial Domain Adaptation for Synthetic Depth Data](https://mlsanity.com/paper/2207.09204/) (2022)
- [Deep Neural Networks in Video Human Action Recognition: A Review](https://mlsanity.com/paper/2305.15692/) (2023)
- [GTA-Crime: A Synthetic Dataset and Generation Framework for Fatal Violence Detection with Adversarial Snippet-Level Domain Adaptation](https://mlsanity.com/paper/2509.08232/) (2025)
- [A cross-process welding penetration status prediction algorithm based on unsupervised domain adaptation in laser and TIG welding](https://mlsanity.com/paper/2606.26078/) (2026)

## Abstract (arXiv)

Endoscopic videos from multicentres often have different imaging conditions, e.g., color and illumination, which make the models trained on one domain usually fail to generalize well to another. Domain adaptation is one of the potential solutions to address the problem. However, few of existing works focused on the translation of video-based data. In this work, we propose a novel generative adversarial network (GAN), namely VideoGAN, to transfer the video-based data across different domains. As the frames of a video may have similar content and imaging conditions, the proposed VideoGAN has an X-shape generator to preserve the intra-video consistency during translation. Furthermore, a loss function, namely color histogram loss, is proposed to tune the color distribution of each translated frame. Two colonoscopic datasets from different centres, i.e., CVC-Clinic and ETIS-Larib, are adopted to evaluate the performance of domain adaptation of our VideoGAN. Experimental results demonstrate that the adapted colonoscopic video generated by our VideoGAN can significantly boost the segmentation accuracy, i.e., an improvement of 5%, of colorectal polyps on multicentre datasets. As our VideoGAN is a general network architecture, we also evaluate its performance with the CamVid driving video dataset on the cloudy-to-sunny translation task. Comprehensive experiments show that the domain gap could be substantially narrowed down by our VideoGAN.