Arbitrary Facial Attribute Editing: Only Change What You Want

By Zhenliang He, Wangmeng Zuo, Meina Kan, Shiguang Shan, Xilin Chen. 2017

57 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Mingkui Tan
3. Wangmeng Zuo
4. Wen Li
5. Yi Yang
6. Ming-Hsuan Yang
7. Yong Guo
8. Yuhua Chen

Citations in the corpus

• FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age (2019, background): “…image datasets [22, 31, 13, 69, 37, 24, 41, 68, 15, 27, 46, 8, 39] have been proposed and fostered research and development for automated face detection [35, 21], alignment [66, 44], recognition [57, 49], generation [67, 5, 26, 58], modification [3, 32, 19], and attribute classification [31, 37].”
• STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing (2019, methodology): “For example, auto-encoder architecture is adopted in [11, 17, 26], and shallow encoder-decoder with residual blocks is used in [7, 34].”
• DLOW: Domain Flow for Adaptation and Generalization (2018, background): “A few recent works [36, 18, 59, 24, 1, 6, 30, 53, 34] concentrates on learning a unified model to translate images into different styles.”
• FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation (2021, background): “To date, numerous large scale face image datasets [21, 31, 13, 70, 37, 23, 43, 69, 14, 26, 48, 8, 40] have been proposed and fostered research and development for automated face detection [35, 20], alignment [67, 46], recognition [57, 51], generation [68, 5, 25, 58], modification [3, 32, 18], and at”
• FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals (2019, methodology): “Following the pattern, applications and explorations of GANs have been done for face completion [37], facial attribute manipulation [49, 16, 24], frontal view synthesis [26], facial reenactment [31, 53, 59], identity-preserving synthesis [8] and expression editing [19].”
• Auto-Embedding Generative Adversarial Networks For High Resolution Image Synthesis (2019, background): “neural networks (DNNs) in many real-world applications, such as texture synthesis [11], [20], [32], [41], super-resolution [15], [30], [50] and attribute editing [9], [16], [55].”
• Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language (2018, methodology): “Similar to [27, 8], our potential extension is to incorporate an explicit control variable into our generator to decide the trade-off example by example.”