# Multi-Channel Attention Selection GANs for Guided Image-to-Image Translation

arXiv: https://arxiv.org/abs/2002.01048 · HTML: https://mlsanity.com/paper/2002.01048/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2020.

## Built on by (most cited first, 23 of 23)

- [MetaEarth: A Generative Foundation Model for Global-Scale Remote Sensing Image Generation](https://mlsanity.com/paper/2405.13570/) (2024)
- [CoMoGAN: continuous model-guided image-to-image translation](https://mlsanity.com/paper/2103.06879/) (2021)
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- [Graph Transformer GANs for Graph-Constrained House Generation](https://mlsanity.com/paper/2303.08225/) (2023)
- [HUMBI: A Large Multiview Dataset of Human Body Expressions and Benchmark Challenge](https://mlsanity.com/paper/2110.00119/) (2021)
- [Pose-driven Attention-guided Image Generation for Person Re-Identification](https://mlsanity.com/paper/2104.13773/) (2021)
- [Physics-informed Guided Disentanglement in Generative Networks](https://mlsanity.com/paper/2107.14229/) (2021)
- [Edge Guided GANs with Multi-Scale Contrastive Learning for Semantic Image Synthesis](https://mlsanity.com/paper/2307.12084/) (2023)
- [SCP-Diff: Spatial-Categorical Joint Prior for Diffusion Based Semantic Image Synthesis](https://mlsanity.com/paper/2403.09638/) (2024)
- [ManiFest: Manifold Deformation for Few-shot Image Translation](https://mlsanity.com/paper/2111.13681/) (2021)
- [Egocentric and Exocentric Methods: A Short Survey](https://mlsanity.com/paper/2410.20621/) (2024)
- [Controllable Image Generation via Collage Representations](https://mlsanity.com/paper/2304.13722/) (2023)
- [Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation](https://mlsanity.com/paper/2401.07721/) (2024)
- [Semi-supervised reference-based sketch extraction using a contrastive learning framework](https://mlsanity.com/paper/2407.14026/) (2024)
- [Improving ClusterGAN Using Self-Augmented Information Maximization of Disentangling Latent Spaces](https://mlsanity.com/paper/2107.12706/) (2021)
- [Leveraging Local Domains for Image-to-Image Translation](https://mlsanity.com/paper/2109.04468/) (2021)
- [A Unified Framework for Biphasic Facial Age Translation with Noisy-Semantic Guided Generative Adversarial Networks](https://mlsanity.com/paper/2109.07373/) (2021)
- [UTSGAN: Unseen Transition Suss GAN for Transition-Aware Image-to-image Translation](https://mlsanity.com/paper/2304.11955/) (2023)
- [SAR-to-Optical Image Translation via Thermodynamics-inspired Network](https://mlsanity.com/paper/2305.13839/) (2023)
- [Diversity-Aware Sign Language Production through a Pose Encoding Variational Autoencoder](https://mlsanity.com/paper/2405.10423/) (2024)
- [Hierarchical Cross-Attention Network for Virtual Try-On](https://mlsanity.com/paper/2411.15542/) (2024)
- [From Competition to Coopetition: Coopetitive Training-Free Image Editing Based on Text Guidance](https://mlsanity.com/paper/2604.15948/) (2026)
- [Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial Networks](https://mlsanity.com/paper/2103.07672/) (2021)

## Abstract (arXiv)

We propose a novel model named Multi-Channel Attention Selection Generative Adversarial Network (SelectionGAN) for guided image-to-image translation, where we translate an input image into another while respecting an external semantic guidance. The proposed SelectionGAN explicitly utilizes the semantic guidance information and consists of two stages. In the first stage, the input image and the conditional semantic guidance are fed into a cycled semantic-guided generation network to produce initial coarse results. In the second stage, we refine the initial results by using the proposed multi-scale spatial pooling & channel selection module and the multi-channel attention selection module. Moreover, uncertainty maps automatically learned from attention maps are used to guide the pixel loss for better network optimization. Exhaustive experiments on four challenging guided image-to-image translation tasks (face, hand, body, and street view) demonstrate that our SelectionGAN is able to generate significantly better results than the state-of-the-art methods. Meanwhile, the proposed framework and modules are unified solutions and can be applied to solve other generation tasks such as semantic image synthesis. The code is available at https://github.com/Ha0Tang/SelectionGAN.