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20182022
most citedSTGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing

20 citations · 32 across the 5 of their papers we have counts for

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cs.CV20227 cited

Self-Supervised Image Restoration with Blurry and Noisy Pairs

Zhilu Zhang, Rongjian Xu, Ming Liu +2

When taking photos under an environment with insufficient light, the exposure time and the sensor gain usually require to be carefully chosen to obtain images with satisfying visua…

cs.CV20222 cited

Adaptive Network Combination for Single-Image Reflection Removal: A Domain Generalization Perspective

Ming Liu, Jianan Pan, Zifei Yan +2

Recently, multiple synthetic and real-world datasets have been built to facilitate the training of deep single image reflection removal (SIRR) models. Meanwhile, diverse testing se…

cs.CV201920 cited

STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing

Ming Liu, Yukang Ding, Min Xia +4

Arbitrary attribute editing generally can be tackled by incorporating encoder-decoder and generative adversarial networks. However, the bottleneck layer in encoder-decoder usually…

cs.CV20182 cited

Learning Symmetry Consistent Deep CNNs for Face Completion

Xiaoming Li, Ming Liu, Jieru Zhu +4

Deep convolutional networks (CNNs) have achieved great success in face completion to generate plausible facial structures. These methods, however, are limited in maintaining global…

cs.CV2018

Learning Warped Guidance for Blind Face Restoration

Xiaoming Li, Ming Liu, Yuting Ye +3

This paper studies the problem of blind face restoration from an unconstrained blurry, noisy, low-resolution, or compressed image (i.e., degraded observation). For better recovery…