most citedAdma-GAN: Attribute-Driven Memory Augmented GANs for Text-to-Image Generation

17 citations · 23 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV202217 cited

Adma-GAN: Attribute-Driven Memory Augmented GANs for Text-to-Image Generation

Xintian Wu, Hanbin Zhao, Liangli Zheng +2

As a challenging task, text-to-image generation aims to generate photo-realistic and semantically consistent images according to the given text descriptions. Existing methods mainl…

cs.CV2022

RBC: Rectifying the Biased Context in Continual Semantic Segmentation

Hanbin Zhao, Fengyu Yang, Xinghe Fu +1

Recent years have witnessed a great development of Convolutional Neural Networks in semantic segmentation, where all classes of training images are simultaneously available. In pra…

cs.CV2021

Progressive Class-based Expansion Learning For Image Classification

Hui Wang, Hanbin Zhao, Xi Li

In this paper, we propose a novel image process scheme called class-based expansion learning for image classification, which aims at improving the supervision-stimulation frequency…

cs.CV20211 cited

When Video Classification Meets Incremental Classes

Hanbin Zhao, Xin Qin, Shihao Su +3

With the rapid development of social media, tremendous videos with new classes are generated daily, which raise an urgent demand for video classification methods that can continuou…

cs.CV2021

PcmNet: Position-Sensitive Context Modeling Network for Temporal Action Localization

Xin Qin, Hanbin Zhao, Guangchen Lin +3

Temporal action localization is an important and challenging task that aims to locate temporal regions in real-world untrimmed videos where actions occur and recognize their classe…

cs.CV20215 cited

Unsupervised Domain Adaptation for Image Classification via Structure-Conditioned Adversarial Learning

Hui Wang, Jian Tian, Songyuan Li +4

Unsupervised domain adaptation (UDA) typically carries out knowledge transfer from a label-rich source domain to an unlabeled target domain by adversarial learning. In principle, e…