activity
20192022
most citedHoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation

7 citations · 20 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20226 cited

Spatial Likelihood Voting with Self-Knowledge Distillation for Weakly Supervised Object Detection

Ze Chen, Zhihang Fu, Jianqiang Huang +5

Weakly supervised object detection (WSOD), which is an effective way to train an object detection model using only image-level annotations, has attracted considerable attention fro…

cs.CV20222 cited

Dynamic Supervisor for Cross-dataset Object Detection

Ze Chen, Zhihang Fu, Jianqiang Huang +6

The application of cross-dataset training in object detection tasks is complicated because the inconsistency in the category range across datasets transforms fully supervised learn…

cs.CV20202 cited

SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection

Ze Chen, Zhihang Fu, Rongxin Jiang +2

Based on the framework of multiple instance learning (MIL), tremendous works have promoted the advances of weakly supervised object detection (WSOD). However, most MIL-based method…

cs.CV20197 cited

HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation

Chao Chen, Zhihang Fu, Zhihong Chen +4

Minimizing the discrepancy of feature distributions between different domains is one of the most promising directions in unsupervised domain adaptation. From the perspective of dis…

cs.CV20193 cited

Towards Self-similarity Consistency and Feature Discrimination for Unsupervised Domain Adaptation

Chao Chen, Zhihang Fu, Zhihong Chen +3

Recent advances in unsupervised domain adaptation mainly focus on learning shared representations by global distribution alignment without considering class information across doma…