activity
20172022
most citedImproving Federated Learning Face Recognition via Privacy-Agnostic Clusters

15 citations · 32 across the 3 of their papers we have counts for

collaborators

9 papers

cs.CV202215 cited

Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters

Qiang Meng, Feng Zhou, Hainan Ren +3

The growing public concerns on data privacy in face recognition can be greatly addressed by the federated learning (FL) paradigm. However, conventional FL methods perform poorly du…

cs.CV202013 cited

Deep Semantic Dictionary Learning for Multi-label Image Classification

Fengtao Zhou, Sheng Huang, Yun Xing

Compared with single-label image classification, multi-label image classification is more practical and challenging. Some recent studies attempted to leverage the semantic informat…

cs.CV2019

Recognizing Part Attributes with Insufficient Data

Xiangyun Zhao, Yi Yang, Feng Zhou +4

Recognizing attributes of objects and their parts is important to many computer vision applications. Although great progress has been made to apply object-level recognition, recogn…

cs.CV2018

Compact Generalized Non-local Network

Kaiyu Yue, Ming Sun, Yuchen Yuan +3

The non-local module is designed for capturing long-range spatio-temporal dependencies in images and videos. Although having shown excellent performance, it lacks the mechanism to…

cs.CV2018

Fine-grained Video Categorization with Redundancy Reduction Attention

Chen Zhu, Xiao Tan, Feng Zhou +4

For fine-grained categorization tasks, videos could serve as a better source than static images as videos have a higher chance of containing discriminative patterns. Nevertheless,…

cs.CV2018

Improving Annotation for 3D Pose Dataset of Fine-Grained Object Categories

Yaming Wang, Xiao Tan, Yi Yang +4

Existing 3D pose datasets of object categories are limited to generic object types and lack of fine-grained information. In this work, we introduce a new large-scale dataset that c…