activity
20202022
most citedGeneralized Domain Conditioned Adaptation Network

69 citations · 102 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV202223 cited

EVA: Exploring the Limits of Masked Visual Representation Learning at Scale

Yuxin Fang, Wen Wang, Binhui Xie +6

We launch EVA, a vision-centric foundation model to explore the limits of visual representation at scale using only publicly accessible data. EVA is a vanilla ViT pre-trained to re…

cs.CV20221 cited

VBLC: Visibility Boosting and Logit-Constraint Learning for Domain Adaptive Semantic Segmentation under Adverse Conditions

Mingjia Li, Binhui Xie, Shuang Li +2

Generalizing models trained on normal visual conditions to target domains under adverse conditions is demanding in the practical systems. One prevalent solution is to bridge the do…

cs.CR20221 cited

Joint Semantic Transfer Network for IoT Intrusion Detection

Jiashu Wu, Yang Wang, Binhui Xie +4

In this paper, we propose a Joint Semantic Transfer Network (JSTN) towards effective intrusion detection for large-scale scarcely labelled IoT domain. As a multi-source heterogeneo…

cs.CV2021

Semantic Distribution-aware Contrastive Adaptation for Semantic Segmentation

Shuang Li, Binhui Xie, Bin Zang +4

Domain adaptive semantic segmentation refers to making predictions on a certain target domain with only annotations of a specific source domain. Current state-of-the-art works sugg…

cs.CV202169 cited

Generalized Domain Conditioned Adaptation Network

Shuang Li, Binhui Xie, Qiuxia Lin +3

Domain Adaptation (DA) attempts to transfer knowledge learned in the labeled source domain to the unlabeled but related target domain without requiring large amounts of target supe…

cs.CV20205 cited

Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation

Shuang Li, Fangrui Lv, Binhui Xie +3

Unsupervised domain adaptation challenges the problem of transferring knowledge from a well-labelled source domain to an unlabelled target domain. Recently,adversarial learning wit…