activity
20182022
most citedStructured Discriminative Tensor Dictionary Learning for Unsupervised Domain Adaptation

2 citations · 3 across the 4 of their papers we have counts for

collaborators

8 papers

cs.CV2022

Cross-View Panorama Image Synthesis

Songsong Wu, Hao Tang, Xiao-Yuan Jing +4

In this paper, we tackle the problem of synthesizing a ground-view panorama image conditioned on a top-view aerial image, which is a challenging problem due to the large gap betwee…

cs.CV20201 cited

Cross-View Image Synthesis with Deformable Convolution and Attention Mechanism

Hao Ding, Songsong Wu, Hao Tang +3

Learning to generate natural scenes has always been a daunting task in computer vision. This is even more laborious when generating images with very different views. When the views…

cs.CV2020

Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learning

Jiamiao Xu, Fangzhao Wang, Qinmu Peng +4

Low-rank Multi-view Subspace Learning (LMvSL) has shown great potential in cross-view classification in recent years. Despite their empirical success, existing LMvSL based methods…

cs.CV2019

Joint Learning of Self-Representation and Indicator for Multi-View Image Clustering

Songsong Wu, Zhiqiang Lu, Hao Tang +4

Multi-view subspace clustering aims to divide a set of multisource data into several groups according to their underlying subspace structure. Although the spectral clustering based…

cs.CV20192 cited

Structured Discriminative Tensor Dictionary Learning for Unsupervised Domain Adaptation

Songsong Wu, Yan Yan, Hao Tang +3

Unsupervised Domain Adaptation (UDA) addresses the problem of performance degradation due to domain shift between training and testing sets, which is common in computer vision appl…

cs.CV2018

Robust Visual Tracking using Multi-Frame Multi-Feature Joint Modeling

Peng Zhang, Shujian Yu, Jiamiao Xu +4

It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations. To cope with this pr…