most citedGenerative and Discriminative Learning for Distorted Image Restoration

2 citations · 2 across the 2 of their papers we have counts for

collaborators

5 papers

eess.IV20202 cited

Generative and Discriminative Learning for Distorted Image Restoration

Yi Gu, Yuting Gao, Jie Li +2

Liquify is a common technique for image editing, which can be used for image distortion. Due to the uncertainty in the distortion variation, restoring distorted images caused by li…

cs.CV2020

Association: Remind Your GAN not to Forget

Yi Gu, Jie Li, Yuting Gao +5

Neural networks are susceptible to catastrophic forgetting. They fail to preserve previously acquired knowledge when adapting to new tasks. Inspired by human associative memory sys…

cs.CV2020

Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning

Jinpeng Wang, Yuting Gao, Ke Li +7

Self-supervised learning has shown great potentials in improving the video representation ability of deep neural networks by getting supervision from the data itself. However, some…

cs.CV2020

Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion

Jinpeng Wang, Yuting Gao, Ke Li +5

One significant factor we expect the video representation learning to capture, especially in contrast with the image representation learning, is the object motion. However, we foun…

cs.CV2018

Double Supervised Network with Attention Mechanism for Scene Text Recognition

Yuting Gao, Zheng Huang, Yuchen Dai +3

In this paper, we propose Double Supervised Network with Attention Mechanism (DSAN), a novel end-to-end trainable framework for scene text recognition. It incorporates one text att…