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20192025
most citedStructured Discriminative Tensor Dictionary Learning for Unsupervised Domain Adaptation

2 citations · 5 across the 7 of their papers we have counts for

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6 papers · 1 filter

cs.CV2022★ 2 cited

Deep Unsupervised Key Frame Extraction for Efficient Video Classification

Hao Tang, Lei Ding, Songsong Wu +3

Video processing and analysis have become an urgent task since a huge amount of videos (e.g., Youtube, Hulu) are uploaded online every day. The extraction of representative key fra…

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.CV2020★ 1 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.CV2019

Expression Conditional GAN for Facial Expression-to-Expression Translation

Hao Tang, Wei Wang, Songsong Wu +4

In this paper, we focus on the facial expression translation task and propose a novel Expression Conditional GAN (ECGAN) which can learn the mapping from one image domain to anothe…

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.CV2019★ 2 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…