126 citations · 331 across the 15 of their papers we have counts for
15 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…
DeLS-3D: Deep Localization and Segmentation with a 3D Semantic Map
Peng Wang, Ruigang Yang, Binbin Cao +2
For applications such as autonomous driving, self-localization/camera pose estimation and scene parsing are crucial technologies. In this paper, we propose a unified framework to t…
Revisiting the Effectiveness of Off-the-shelf Temporal Modeling Approaches for Large-scale Video Classification
Yunlong Bian, Chuang Gan, Xiao Liu +7
This paper describes our solution for the video recognition task of ActivityNet Kinetics challenge that ranked the 1st place. Most of existing state-of-the-art video recognition ap…
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…
Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition
Xiao Liu, Jiang Wang, Shilei Wen +2
A key challenge in fine-grained recognition is how to find and represent discriminative local regions. Recent attention models are capable of learning discriminative region localiz…
Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection
Yu Xiang, Wongun Choi, Yuanqing Lin +1
In CNN-based object detection methods, region proposal becomes a bottleneck when objects exhibit significant scale variation, occlusion or truncation. In addition, these methods ma…
Fully Convolutional Attention Networks for Fine-Grained Recognition
Xiao Liu, Tian Xia, Jiang Wang +3
Fine-grained recognition is challenging due to its subtle local inter-class differences versus large intra-class variations such as poses. A key to address this problem is to local…
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…
Object-centric Sampling for Fine-grained Image Classification
Xiaoyu Wang, Tianbao Yang, Guobin Chen +1
This paper proposes to go beyond the state-of-the-art deep convolutional neural network (CNN) by incorporating the information from object detection, focusing on dealing with fine-…
Generic Object Detection With Dense Neural Patterns and Regionlets
Will Y. Zou, Xiaoyu Wang, Miao Sun +1
This paper addresses the challenge of establishing a bridge between deep convolutional neural networks and conventional object detection frameworks for accurate and efficient gener…
Fine-Grained Visual Categorization via Multi-stage Metric Learning
Qi Qian, Rong Jin, Shenghuo Zhu +1
Fine-grained visual categorization (FGVC) is to categorize objects into subordinate classes instead of basic classes. One major challenge in FGVC is the co-occurrence of two issues…
Analysis of Distributed Stochastic Dual Coordinate Ascent
Tianbao Yang, Shenghuo Zhu, Rong Jin +1
In \citep{Yangnips13}, the author presented distributed stochastic dual coordinate ascent (DisDCA) algorithms for solving large-scale regularized loss minimization. Extraordinary p…
Influence Analysis in the Blogosphere
Michinari Momma, Yun Chi, Yuanqing Lin +2
In this paper we analyze influence in the blogosphere. Recently, influence analysis has become an increasingly important research topic, as online communities, such as social netwo…