Qingshan Liu

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Unsupervised Spatial-spectral Network Learning for Hyperspectral Compressive Snapshot Reconstruction

Hyperspectral compressive imaging takes advantage of compressive sensing...
12/18/2020 ∙ by Yubao Sun, et al. ∙ 0 ∙ share

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Meta-Learning with Network Pruning

Meta-learning is a powerful paradigm for few-shot learning. Although wit...
07/07/2020 ∙ by Hongduan Tian, et al. ∙ 0 ∙ share

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Hyperspectral Image Classification with Attention Aided CNNs

Convolutional neural networks (CNNs) have been widely used for hyperspec...
05/25/2020 ∙ by Renlong Hang, et al. ∙ 14 ∙ share

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Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection

Co-saliency detection aims to discover the common and salient foreground...
03/13/2020 ∙ by Kaihua Zhang, et al. ∙ 0 ∙ share

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Dual Temporal Memory Network for Efficient Video Object Segmentation

Video Object Segmentation (VOS) is typically formulated in a semi-superv...
03/13/2020 ∙ by Kaihua Zhang, et al. ∙ 3 ∙ share

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Classification of Hyperspectral and LiDAR Data Using Coupled CNNs

In this paper, we propose an efficient and effective framework to fuse h...
02/04/2020 ∙ by Renlong Hang, et al. ∙ 15 ∙ share

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Video Saliency Prediction Using Enhanced Spatiotemporal Alignment Network

Due to a variety of motions across different frames, it is highly challe...
01/02/2020 ∙ by Jin Chen, et al. ∙ 13 ∙ share

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Deep Object Co-segmentation via Spatial-Semantic Network Modulation

Object co-segmentation is to segment the shared objects in multiple rele...
11/29/2019 ∙ by Kaihua Zhang, et al. ∙ 1 ∙ share

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Cascaded Recurrent Neural Networks for Hyperspectral Image Classification

By considering the spectral signature as a sequence, recurrent neural ne...
02/28/2019 ∙ by Renlong Hang, et al. ∙ 10 ∙ share

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Matrix Completion with Nonuniform Sampling: Theories and Methods

Prevalent matrix completion theories reply on an assumption that the loc...
05/07/2018 ∙ by Guangcan Liu, et al. ∙ 0 ∙ share

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Fast and Robust Subspace Clustering Using Random Projections

Over the past several decades, subspace clustering has been receiving in...
03/30/2018 ∙ by Guangcan Liu, et al. ∙ 0 ∙ share

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Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

This paper proposes a novel deep learning framework named bidirectional-...
03/23/2017 ∙ by Qingshan Liu, et al. ∙ 0 ∙ share

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Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximization

Iterative Hard Thresholding (IHT) is a class of projected gradient desce...
03/01/2017 ∙ by Bo Liu, et al. ∙ 0 ∙ share

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Unsupervised Video Segmentation via Spatio-Temporally Nonlocal Appearance Learning

Video object segmentation is challenging due to the factors like rapidly...
12/24/2016 ∙ by Kaihua Zhang, et al. ∙ 0 ∙ share

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Learning Multi-Scale Deep Features for High-Resolution Satellite Image Classification

In this paper, we propose a multi-scale deep feature learning method for...
11/11/2016 ∙ by Qingshan Liu, et al. ∙ 0 ∙ share

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Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification

Convolutional neural networks (CNNs) have attracted increasing attention...
11/11/2016 ∙ by Qingshan Liu, et al. ∙ 0 ∙ share

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Visual Tracking via Boolean Map Representations

In this paper, we present a simple yet effective Boolean map based repre...
10/30/2016 ∙ by Kaihua Zhang, et al. ∙ 0 ∙ share

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Elastic Net Hypergraph Learning for Image Clustering and Semi-supervised Classification

Graph model is emerging as a very effective tool for learning the comple...
03/03/2016 ∙ by Qingshan Liu, et al. ∙ 0 ∙ share

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Adaptive Compressive Tracking via Online Vector Boosting Feature Selection

Recently, the compressive tracking (CT) method has attracted much attent...
04/21/2015 ∙ by Qingshan Liu, et al. ∙ 0 ∙ share

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Robust Visual Tracking via Convolutional Networks

Deep networks have been successfully applied to visual tracking by learn...
01/19/2015 ∙ by Kaihua Zhang, et al. ∙ 0 ∙ share

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Qingshan Liu

Featured Co-authors

Dacheng Tao

Ming-Hsuan Yang

Meng Wang

Bo Liu

Tongliang Liu

Dong Liu

Jing Yang

Zhao Zhang

Mohan Kankanhalli

Yi Wu

Feng Zhou

Unsupervised Spatial-spectral Network Learning for Hyperspectral Compressive Snapshot Reconstruction

Meta-Learning with Network Pruning

Hyperspectral Image Classification with Attention Aided CNNs

Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection

Dual Temporal Memory Network for Efficient Video Object Segmentation

Classification of Hyperspectral and LiDAR Data Using Coupled CNNs

Video Saliency Prediction Using Enhanced Spatiotemporal Alignment Network

Deep Object Co-segmentation via Spatial-Semantic Network Modulation

Cascaded Recurrent Neural Networks for Hyperspectral Image Classification

Matrix Completion with Nonuniform Sampling: Theories and Methods

Fast and Robust Subspace Clustering Using Random Projections

Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximization

Unsupervised Video Segmentation via Spatio-Temporally Nonlocal Appearance Learning

Learning Multi-Scale Deep Features for High-Resolution Satellite Image Classification

Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification

Visual Tracking via Boolean Map Representations

Elastic Net Hypergraph Learning for Image Clustering and Semi-supervised Classification

Adaptive Compressive Tracking via Online Vector Boosting Feature Selection

Robust Visual Tracking via Convolutional Networks