69 citations · 200 across the 10 of their papers we have counts for
10 papers
CoDS: Enhancing Collaborative Perception in Heterogeneous Scenarios via Domain Separation
Yushan Han, Hui Zhang, Honglei Zhang +3
Collaborative perception has been proven to improve individual perception in autonomous driving through multi-agent interaction. Nevertheless, most methods often assume identical e…
Channel Consistency Prior and Self-Reconstruction Strategy Based Unsupervised Image Deraining
Guanglu Dong, Tianheng Zheng, Yuanzhouhan Cao +2
Recently, deep image deraining models based on paired datasets have made a series of remarkable progress. However, they cannot be well applied in real-world applications due to the…
Random Sub-Samples Generation for Self-Supervised Real Image Denoising
Yizhong Pan, Xiao Liu, Xiangyu Liao +2
With sufficient paired training samples, the supervised deep learning methods have attracted much attention in image denoising because of their superior performance. However, it is…
Robust Data Hiding Using Inverse Gradient Attention
Honglei Zhang, Hu Wang, Yuanzhouhan Cao +2
Data hiding is the procedure of encoding desired information into a certain types of cover media (e.g. images) to resist potential noises for data recovery, while ensuring the embe…
MuMMER: Socially Intelligent Human-Robot Interaction in Public Spaces
Mary Ellen Foster, Bart Craenen, Amol Deshmukh +27
In the EU-funded MuMMER project, we have developed a social robot designed to interact naturally and flexibly with users in public spaces such as a shopping mall. We present the la…
Exploiting temporal consistency for real-time video depth estimation
Haokui Zhang, Chunhua Shen, Ying Li +3
Accuracy of depth estimation from static images has been significantly improved recently, by exploiting hierarchical features from deep convolutional neural networks (CNNs). Compar…
Monocular Depth Estimation with Augmented Ordinal Depth Relationships
Yuanzhouhan Cao, Tianqi Zhao, Ke Xian +3
Most existing algorithms for depth estimation from single monocular images need large quantities of metric groundtruth depths for supervised learning. We show that relative depth c…
Exploiting Depth from Single Monocular Images for Object Detection and Semantic Segmentation
Yuanzhouhan Cao, Chunhua Shen, Heng Tao Shen
Augmenting RGB data with measured depth has been shown to improve the performance of a range of tasks in computer vision including object detection and semantic segmentation. Altho…
Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks
Yuanzhouhan Cao, Zifeng Wu, Chunhua Shen
Depth estimation from single monocular images is a key component of scene understanding and has benefited largely from deep convolutional neural networks (CNN) recently. In this ar…
Temporal Pyramid Pooling Based Convolutional Neural Networks for Action Recognition
Peng Wang, Yuanzhouhan Cao, Chunhua Shen +2
Encouraged by the success of Convolutional Neural Networks (CNNs) in image classification, recently much effort is spent on applying CNNs to video based action recognition problems…