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20182022
most citedAdversarial Style Mining for One-Shot Unsupervised Domain Adaptation

66 citations · 80 across the 8 of their papers we have counts for

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8 papers · 1 filter

cs.CV20223 cited

Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning

Feifei Shao, Yawei Luo, Ping Liu +4

The expensive annotation cost is notoriously known as the main constraint for the development of the point cloud semantic segmentation technique. Active learning methods endeavor t…

cs.CV20213 cited

Automated Deepfake Detection

Ping Liu, Yuewei Lin, Yang He +5

In this paper, we propose to utilize Automated Machine Learning to adaptively search a neural architecture for deepfake detection. This is the first time to employ automated machin…

cs.CV2021

Adversarial Semantic Hallucination for Domain Generalized Semantic Segmentation

Gabriel Tjio, Ping Liu, Joey Tianyi Zhou +1

Convolutional neural networks typically perform poorly when the test (target domain) and training (source domain) data have significantly different distributions. While this proble…

cs.CV202066 cited

Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation

Yawei Luo, Ping Liu, Tao Guan +2

We aim at the problem named One-Shot Unsupervised Domain Adaptation. Unlike traditional Unsupervised Domain Adaptation, it assumes that only one unlabeled target sample can be avai…

cs.CV2020

Face Hallucination with Finishing Touches

Yang Zhang, Ivor W. Tsang, Jun Li +3

Obtaining a high-quality frontal face image from a low-resolution (LR) non-frontal face image is primarily important for many facial analysis applications. However, mainstreams eit…

cs.CV20193 cited

Very Long Natural Scenery Image Prediction by Outpainting

Zongxin Yang, Jian Dong, Ping Liu +2

Comparing to image inpainting, image outpainting receives less attention due to two challenges in it. The first challenge is how to keep the spatial and content consistency between…