activity
20192021
most citedNoisy-As-Clean: Learning Self-supervised Denoising from the Corrupted Image

192 citations · 198 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2021★ 2 cited

Repetitive Activity Counting by Sight and Sound

Yunhua Zhang, Ling Shao, Cees G. M. Snoek

This paper strives for repetitive activity counting in videos. Different from existing works, which all analyze the visual video content only, we incorporate for the first time the…

cs.LG2020

Group Whitening: Balancing Learning Efficiency and Representational Capacity

Lei Huang, Yi Zhou, Li Liu +2

Batch normalization (BN) is an important technique commonly incorporated into deep learning models to perform standardization within mini-batches. The merits of BN in improving a m…

cs.CV2019★ 1 cited

Diversifying Inference Path Selection: Moving-Mobile-Network for Landmark Recognition

Biao Qian, Yang Wang, Zhao Zhang +3

Deep convolutional neural networks have largely benefited computer vision tasks. However, the high computational complexity limits their real-world applications. To this end, many…

cs.CV2019★ 3 cited

Dual-reference Age Synthesis

Yuan Zhou, Bingzhang Hu, and Jun He +2

Age synthesis methods typically take a single image as input and use a specific number to control the age of the generated image. In this paper, we propose a novel framework taking…

cs.CV2019★ 192 cited

Noisy-As-Clean: Learning Self-supervised Denoising from the Corrupted Image

Jun Xu, Yuan Huang, Ming-Ming Cheng +4

Supervised deep networks have achieved promisingperformance on image denoising, by learning image priors andnoise statistics on plenty pairs of noisy and clean images. Unsupervised…