activity
20182021
most citedDomain Alignment with Triplets

10 citations · 13 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2021

Ranking Models in Unlabeled New Environments

Xiaoxiao Sun, Yunzhong Hou, Weijian Deng +2

Consider a scenario where we are supplied with a number of ready-to-use models trained on a certain source domain and hope to directly apply the most appropriate ones to different…

cs.CV20212 cited

What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?

Weijian Deng, Stephen Gould, Liang Zheng

Understanding classifier decision under novel environments is central to the community, and a common practice is evaluating it on labeled test sets. However, in real-world testing,…

cs.CV20201 cited

Fine-grained Classification via Categorical Memory Networks

Weijian Deng, Joshua Marsh, Stephen Gould +1

Motivated by the desire to exploit patterns shared across classes, we present a simple yet effective class-specific memory module for fine-grained feature learning. The memory modu…

cs.CV201910 cited

Domain Alignment with Triplets

Weijian Deng, Liang Zheng, Jianbin Jiao

Deep domain adaptation methods can reduce the distribution discrepancy by learning domain-invariant embedddings. However, these methods only focus on aligning the whole data distri…

cs.CV2018

Similarity-preserving Image-image Domain Adaptation for Person Re-identification

Weijian Deng, Liang Zheng, Qixiang Ye +2

This article studies the domain adaptation problem in person re-identification (re-ID) under a "learning via translation" framework, consisting of two components, 1) translating th…