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20192024
most citedTrustable Co-label Learning from Multiple Noisy Annotators

28 citations · 93 across the 9 of their papers we have counts for

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cs.CV2022

Scale Attention for Learning Deep Face Representation: A Study Against Visual Scale Variation

Hailin Shi, Hang Du, Yibo Hu +3

Human face images usually appear with wide range of visual scales. The existing face representations pursue the bandwidth of handling scale variation via multi-scale scheme that as…

cs.CV2021

Towards NIR-VIS Masked Face Recognition

Hang Du, Hailin Shi, Yinglu Liu +2

Near-infrared to visible (NIR-VIS) face recognition is the most common case in heterogeneous face recognition, which aims to match a pair of face images captured from two different…

cs.CV20201 cited

Semi-Siamese Training for Shallow Face Learning

Hang Du, Hailin Shi, Yuchi Liu +4

Most existing public face datasets, such as MS-Celeb-1M and VGGFace2, provide abundant information in both breadth (large number of IDs) and depth (sufficient number of samples) fo…

cs.CV202022 cited

A survey of face recognition techniques under occlusion

Dan Zeng, Raymond Veldhuis, Luuk Spreeuwers

The limited capacity to recognize faces under occlusions is a long-standing problem that presents a unique challenge for face recognition systems and even for humans. The problem r…

cs.CV20203 cited

Robust Visual Object Tracking with Two-Stream Residual Convolutional Networks

Ning Zhang, Jingen Liu, Ke Wang +2

The current deep learning based visual tracking approaches have been very successful by learning the target classification and/or estimation model from a large amount of supervised…

cs.CV20198 cited

Zooming into Face Forensics: A Pixel-level Analysis

Jia Li, Tong Shen, Wei Zhang +3

The stunning progress in face manipulation methods has made it possible to synthesize realistic fake face images, which poses potential threats to our society. It is urgent to have…