74 citations · 185 across the 16 of their papers we have counts for
7 papers · 1 filter
PVRNet: Point-View Relation Neural Network for 3D Shape Recognition
Haoxuan You, Yifan Feng, Xibin Zhao +3
Three-dimensional (3D) shape recognition has drawn much research attention in the field of computer vision. The advances of deep learning encourage various deep models for 3D featu…
Flexible Attributed Network Embedding
Enya Shen, Zhidong Cao, Changqing Zou +1
Network embedding aims to find a way to encode network by learning an embedding vector for each node in the network. The network often has property information which is highly info…
Sketch-R2CNN: An Attentive Network for Vector Sketch Recognition
Lei Li, Changqing Zou, Youyi Zheng +3
Freehand sketching is a dynamic process where points are sequentially sampled and grouped as strokes for sketch acquisition on electronic devices. To recognize a sketched object, m…
ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understanding
Ning Liu, Yongchao Long, Changqing Zou +3
We propose an attention-injective deformable convolutional network called ADCrowdNet for crowd understanding that can address the accuracy degradation problem of highly congested n…
LUCSS: Language-based User-customized Colourization of Scene Sketches
Changqing Zou, Haoran Mo, Ruofei Du +3
We introduce LUCSS, a language-based system for interactive col- orization of scene sketches, based on their semantic understanding. LUCSS is built upon deep neural networks traine…
SketchyScene: Richly-Annotated Scene Sketches
Changqing Zou, Qian Yu, Ruofei Du +6
We contribute the first large-scale dataset of scene sketches, SketchyScene, with the goal of advancing research on sketch understanding at both the object and scene level. The dat…
Memory Attention Networks for Skeleton-based Action Recognition
Chunyu Xie, Ce Li, Baochang Zhang +4
Skeleton-based action recognition task is entangled with complex spatio-temporal variations of skeleton joints, and remains challenging for Recurrent Neural Networks (RNNs). In thi…