activity
20172020
most citedDeepUNet: A Deep Fully Convolutional Network for Pixel-level Sea-Land Segmentation

31 citations · 34 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV20201 cited

P-DIFF: Learning Classifier with Noisy Labels based on Probability Difference Distributions

Wei Hu, QiHao Zhao, Yangyu Huang +1

Learning deep neural network (DNN) classifier with noisy labels is a challenging task because the DNN can easily over-fit on these noisy labels due to its high capability. In this…

cs.CV2019

Noise-Tolerant Paradigm for Training Face Recognition CNNs

Wei Hu, Yangyu Huang, Fan Zhang +1

Benefit from large-scale training datasets, deep Convolutional Neural Networks(CNNs) have achieved impressive results in face recognition(FR). However, tremendous scale of datasets…

cs.CV2018

TreeSegNet: Adaptive Tree CNNs for Subdecimeter Aerial Image Segmentation

Kai Yue, Lei Yang, Ruirui Li +3

For the task of subdecimeter aerial imagery segmentation, fine-grained semantic segmentation results are usually difficult to obtain because of complex remote sensing content and o…

cs.CV2018

SeqFace: Make full use of sequence information for face recognition

Wei Hu, Yangyu Huang, Fan Zhang +3

Deep convolutional neural networks (CNNs) have greatly improved the Face Recognition (FR) performance in recent years. Almost all CNNs in FR are trained on the carefully labeled da…

cs.CV201731 cited

DeepUNet: A Deep Fully Convolutional Network for Pixel-level Sea-Land Segmentation

Ruirui Li, Wenjie Liu, Lei Yang +4

Semantic segmentation is a fundamental research in remote sensing image processing. Because of the complex maritime environment, the sea-land segmentation is a challenging task. Al…

cs.DC20172 cited

A GPU Based Memory Optimized Parallel Method For FFT Implementation

Fan Zhang, Chen Hu, Qiang Yin +1

FFT (fast Fourier transform) plays a very important role in many fields, such as digital signal processing, digital image processing and so on. However, in application, FFT becomes…