activity
20192021
most citedDiscriminative Cross-Domain Feature Learning for Partial Domain Adaptation

5 citations · 9 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV2021

Towards Novel Target Discovery Through Open-Set Domain Adaptation

Taotao Jing, Hongfu Liu, Zhengming Ding

Open-set domain adaptation (OSDA) considers that the target domain contains samples from novel categories unobserved in external source domain. Unfortunately, existing OSDA methods…

cs.CV2020

Towards Fair Knowledge Transfer for Imbalanced Domain Adaptation

Taotao Jing, Bingrong Xu, Jingjing Li +1

Domain adaptation (DA) becomes an up-and-coming technique to address the insufficient or no annotation issue by exploiting external source knowledge. Existing DA algorithms mainly…

cs.CV20201 cited

Adversarial Dual Distinct Classifiers for Unsupervised Domain Adaptation

Taotao Jing, Zhengming Ding

Unsupervised Domain adaptation (UDA) attempts to recognize the unlabeled target samples by building a learning model from a differently-distributed labeled source domain. Conventio…

cs.CV20203 cited

Adaptively-Accumulated Knowledge Transfer for Partial Domain Adaptation

Taotao Jing, Haifeng Xia, Zhengming Ding

Partial domain adaptation (PDA) attracts appealing attention as it deals with a realistic and challenging problem when the source domain label space substitutes the target domain.…

cs.CV20205 cited

Discriminative Cross-Domain Feature Learning for Partial Domain Adaptation

Taotao Jing, Ming Shao, Zhengming Ding

Partial domain adaptation aims to adapt knowledge from a larger and more diverse source domain to a smaller target domain with less number of classes, which has attracted appealing…

cs.CV2019

EV-Action: Electromyography-Vision Multi-Modal Action Dataset

Lichen Wang, Bin Sun, Joseph Robinson +2

Multi-modal human action analysis is a critical and attractive research topic. However, the majority of the existing datasets only provide visual modalities (i.e., RGB, depth and s…