71 citations · 234 across the 20 of their papers we have counts for
13 papers · 1 filter
Leveraging Systematic Knowledge of 2D Transformations
Jiachen Kang, Wenjing Jia, Xiangjian He
The existing deep learning models suffer from out-of-distribution (o.o.d.) performance drop in computer vision tasks. In comparison, humans have a remarkable ability to interpret i…
CAR: Class-aware Regularizations for Semantic Segmentation
Ye Huang, Di Kang, Liang Chen +4
Recent segmentation methods, such as OCR and CPNet, utilizing "class level" information in addition to pixel features, have achieved notable success for boosting the accuracy of ex…
Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation
Ye Huang, Di Kang, Wenjing Jia +2
Spatial and channel attentions, modelling the semantic interdependencies in spatial and channel dimensions respectively, have recently been widely used for semantic segmentation. H…
PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting
Saeed Amirgholipour, Xiangjian He, Wenjing Jia +2
Crowd counting, i.e., estimating the number of people in a crowded area, has attracted much interest in the research community. Although many attempts have been reported, crowd cou…
See More Than Once -- Kernel-Sharing Atrous Convolution for Semantic Segmentation
Ye Huang, Qingqing Wang, Wenjing Jia +1
The state-of-the-art semantic segmentation solutions usually leverage different receptive fields via multiple parallel branches to handle objects with different sizes. However, emp…
DENet: A Universal Network for Counting Crowd with Varying Densities and Scales
Lei Liu, Jie Jiang, Wenjing Jia +3
Counting people or objects with significantly varying scales and densities has attracted much interest from the research community and yet it remains an open problem. In this paper…
FACLSTM: ConvLSTM with Focused Attention for Scene Text Recognition
Qingqing Wang, Wenjing Jia, Xiangjian He +3
Scene text recognition has recently been widely treated as a sequence-to-sequence prediction problem, where traditional fully-connected-LSTM (FC-LSTM) has played a critical role. D…
A-CCNN: adaptive ccnn for density estimation and crowd counting
Saeed Amirgholipour Kasmani, Xiangjian He, Wenjing Jia +2
Crowd counting, for estimating the number of people in a crowd using vision-based computer techniques, has attracted much interest in the research community. Although many attempts…
Beyond Context: Exploring Semantic Similarity for Tiny Face Detection
Yue Xi, Jiangbin Zheng, Xiangjian He +2
Tiny face detection aims to find faces with high degrees of variability in scale, resolution and occlusion in cluttered scenes. Due to the very little information available on tiny…
A Structural Correlation Filter Combined with A Multi-task Gaussian Particle Filter for Visual Tracking
Manna Dai, Shuying Cheng, Xiangjian He +1
In this paper, we propose a novel structural correlation filter combined with a multi-task Gaussian particle filter (KCF-GPF) model for robust visual tracking. We first present an…
Structured Inhomogeneous Density Map Learning for Crowd Counting
Hanhui Li, Xiangjian He, Hefeng Wu +4
In this paper, we aim at tackling the problem of crowd counting in extremely high-density scenes, which contain hundreds, or even thousands of people. We begin by a comprehensive a…
Soft Locality Preserving Map (SLPM) for Facial Expression Recognition
Cigdem Turan, Kin-Man Lam, Xiangjian He
For image recognition, an extensive number of methods have been proposed to overcome the high-dimensionality problem of feature vectors being used. These methods vary from unsuperv…
Face Parsing via a Fully-Convolutional Continuous CRF Neural Network
Lei Zhou, Zhi Liu, Xiangjian He
In this work, we address the face parsing task with a Fully-Convolutional continuous CRF Neural Network (FC-CNN) architecture. In contrast to previous face parsing methods that app…