# Attention-GAN for Object Transfiguration in Wild Images

arXiv: https://arxiv.org/abs/1803.06798 · HTML: https://mlsanity.com/paper/1803.06798/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2018.

## Built on by (most cited first, 30 of 32)

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- [Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos](https://mlsanity.com/paper/2203.13704/) (2022)
- [Region-aware Knowledge Distillation for Efficient Image-to-Image Translation](https://mlsanity.com/paper/2205.12451/) (2022)
- [Edge Guided GANs with Multi-Scale Contrastive Learning for Semantic Image Synthesis](https://mlsanity.com/paper/2307.12084/) (2023)
- [Multi-cropping Contrastive Learning and Domain Consistency for Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/2304.12235/) (2023)
- [Power Reinforcement Post-Training of Text-to-Image Models with Super-Linear Advantage Shaping](https://mlsanity.com/paper/2605.10937/) (2026)
- [Non-uniform Motion Deblurring with Blurry Component Divided Guidance](https://mlsanity.com/paper/2101.06021/) (2021)
- [ECG-ATK-GAN: Robustness against Adversarial Attacks on ECGs using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2110.09983/) (2021)
- [DGFont++: Robust Deformable Generative Networks for Unsupervised Font Generation](https://mlsanity.com/paper/2212.14742/) (2022)
- [Ultrasound Image Enhancement using CycleGAN and Perceptual Loss](https://mlsanity.com/paper/2312.11748/) (2023)
- [Contrastive Learning and Cycle Consistency-based Transductive Transfer Learning for Target Annotation](https://mlsanity.com/paper/2401.12340/) (2024)
- [Culture-to-Culture Image Translation and User Evaluation](https://mlsanity.com/paper/2201.01565/) (2022)
- [DSI2I: Dense Style for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2212.13253/) (2022)
- [Guided Image-to-Image Translation by Discriminator-Generator Communication](https://mlsanity.com/paper/2303.03598/) (2023)
- [SDA-GAN: Unsupervised Image Translation Using Spectral Domain Attention-Guided Generative Adversarial Network](https://mlsanity.com/paper/2110.02873/) (2021)
- [Global and Local Alignment Networks for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2111.10346/) (2021)
- [RCC-GAN: Regularized Compound Conditional GAN for Large-Scale Tabular Data Synthesis](https://mlsanity.com/paper/2205.11693/) (2022)

## Abstract (arXiv)

This paper studies the object transfiguration problem in wild images. The generative network in classical GANs for object transfiguration often undertakes a dual responsibility: to detect the objects of interests and to convert the object from source domain to target domain. In contrast, we decompose the generative network into two separat networks, each of which is only dedicated to one particular sub-task. The attention network predicts spatial attention maps of images, and the transformation network focuses on translating objects. Attention maps produced by attention network are encouraged to be sparse, so that major attention can be paid to objects of interests. No matter before or after object transfiguration, attention maps should remain constant. In addition, learning attention network can receive more instructions, given the available segmentation annotations of images. Experimental results demonstrate the necessity of investigating attention in object transfiguration, and that the proposed algorithm can learn accurate attention to improve quality of generated images.