1.1k citations · 2.2k across the 32 of their papers we have counts for
19 papers · 1 filter
Robust Depth Completion with Uncertainty-Driven Loss Functions
Yufan Zhu, Weisheng Dong, Leida Li +3
Recovering a dense depth image from sparse LiDAR scans is a challenging task. Despite the popularity of color-guided methods for sparse-to-dense depth completion, they treated pixe…
PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network
Pengfei Wang, Chengquan Zhang, Fei Qi +7
The reading of arbitrarily-shaped text has received increasing research attention. However, existing text spotters are mostly built on two-stage frameworks or character-based metho…
Optical Flow Estimation via Motion Feature Recovery
Yang Jiao, Guangming Shi, Trac D. Tran
Optical flow estimation with occlusion or large displacement is a problematic challenge due to the lost of corresponding pixels between consecutive frames. In this paper, we discov…
Temporal Graph Modeling for Skeleton-based Action Recognition
Jianan Li, Xuemei Xie, Zhifu Zhao +3
Graph Convolutional Networks (GCNs), which model skeleton data as graphs, have obtained remarkable performance for skeleton-based action recognition. Particularly, the temporal dyn…
2D+3D Facial Expression Recognition via Discriminative Dynamic Range Enhancement and Multi-Scale Learning
Yang Jiao, Yi Niu, Trac D. Tran +1
In 2D+3D facial expression recognition (FER), existing methods generate multi-view geometry maps to enhance the depth feature representation. However, this may introduce false esti…
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation
Yang Jiao, Trac D. Tran, Guangming Shi
This paper addresses the challenging unsupervised scene flow estimation problem by jointly learning four low-level vision sub-tasks: optical flow , stereo-depth $\textb…
A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition
Yang Li, Boxun Fu, Fu Li +2
The existed methods for electroencephalograph (EEG) emotion recognition always train the models based on all the EEG samples indistinguishably. However, some of the source (trainin…
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network
Qian Ning, Weisheng Dong, Guangming Shi +2
Deep neural networks (DNNs) based methods have achieved great success in single image super-resolution (SISR). However, existing state-of-the-art SISR techniques are designed like…
A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning
Pengfei Wang, Chengquan Zhang, Fei Qi +6
Detecting scene text of arbitrary shapes has been a challenging task over the past years. In this paper, we propose a novel segmentation-based text detector, namely SAST, which emp…
The Shape of Sight: A Homological Framework for Unifying Visual Perception
Xin Li
Visual perception, the brain's construction of a stable world from sensory data, faces several long-standing, fundamental challenges. While often studied separately, these problems…
Full Image Recover for Block-Based Compressive Sensing
Xuemei Xie, Chenye Wang, Jiang Du +1
Recent years, compressive sensing (CS) has improved greatly for the application of deep learning technology. For convenience, the input image is usually measured and reconstructed…
Perceptual Compressive Sensing
Jiang Du, Xuemei Xie, Chenye Wang +1
Compressive sensing (CS) works to acquire measurements at sub-Nyquist rate and recover the scene images. Existing CS methods always recover the scene images in pixel level. This ca…
ConvCSNet: A Convolutional Compressive Sensing Framework Based on Deep Learning
Xiaotong Lu, Weisheng Dong, Peiyao Wang +2
Compressive sensing (CS), aiming to reconstruct an image/signal from a small set of random measurements has attracted considerable attentions in recent years. Due to the high dimen…
Denoising Prior Driven Deep Neural Network for Image Restoration
Weisheng Dong, Peiyao Wang, Wotao Yin +3
Deep neural networks (DNNs) have shown very promising results for various image restoration (IR) tasks. However, the design of network architectures remains a major challenging for…
Fully Convolutional Measurement Network for Compressive Sensing Image Reconstruction
Jiang Du, Xuemei Xie, Chenye Wang +3
Recently, deep learning methods have made a significant improvement in compressive sensing image reconstruction task. In the existing methods, the scene is measured block by block…
Real-Time Illegal Parking Detection System Based on Deep Learning
Xuemei Xie, Chenye Wang, Shu Chen +2
The increasing illegal parking has become more and more serious. Nowadays the methods of detecting illegally parked vehicles are based on background segmentation. However, this met…
Adaptive Measurement Network for CS Image Reconstruction
Xuemei Xie, Yuxiang Wang, Guangming Shi +3
Conventional compressive sensing (CS) reconstruction is very slow for its characteristic of solving an optimization problem. Convolu- tional neural network can realize fast process…
Feature-Fused SSD: Fast Detection for Small Objects
Guimei Cao, Xuemei Xie, Wenzhe Yang +3
Small objects detection is a challenging task in computer vision due to its limited resolution and information. In order to solve this problem, the majority of existing methods sac…
Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization
Weisheng Dong, Lei Zhang, Guangming Shi +1
As a powerful statistical image modeling technique, sparse representation has been successfully used in various image restoration applications. The success of sparse representation…