131 citations · 248 across the 13 of their papers we have counts for
13 papers
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…
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…
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…
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…
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,…
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…
Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition
Ming Sun, Yuchen Yuan, Feng Zhou +1
Attention-based learning for fine-grained image recognition remains a challenging task, where most of the existing methods treat each object part in isolation, while neglecting the…
3D Pose Estimation for Fine-Grained Object Categories
Yaming Wang, Xiao Tan, Yi Yang +4
Existing object pose estimation datasets are related to generic object types and there is so far no dataset for fine-grained object categories. In this work, we introduce a new lar…
Deep Metric Learning with Angular Loss
Jian Wang, Feng Zhou, Shilei Wen +2
The modern image search system requires semantic understanding of image, and a key yet under-addressed problem is to learn a good metric for measuring the similarity between images…
Dynamic Computational Time for Visual Attention
Zhichao Li, Yi Yang, Xiao Liu +3
We propose a dynamic computational time model to accelerate the average processing time for recurrent visual attention (RAM). Rather than attention with a fixed number of steps for…
Fine-grained Image Classification by Exploring Bipartite-Graph Labels
Feng Zhou, Yuanqing Lin
Given a food image, can a fine-grained object recognition engine tell "which restaurant which dish" the food belongs to? Such ultra-fine grained image recognition is the key for ma…
Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loop
Yin Cui, Feng Zhou, Yuanqing Lin +1
Existing fine-grained visual categorization methods often suffer from three challenges: lack of training data, large number of fine-grained categories, and high intraclass vs. low…
Embedding Label Structures for Fine-Grained Feature Representation
Xiaofan Zhang, Feng Zhou, Yuanqing Lin +1
Recent algorithms in convolutional neural networks (CNN) considerably advance the fine-grained image classification, which aims to differentiate subtle differences among subordinat…