536 citations · 1.3k across the 27 of their papers we have counts for
27 papers
Remote Sensing Object Counting with Online Knowledge Learning
Shengqin Jiang, Yuan Gao, Bowen Li +3
Efficient models for remote sensing object counting are urgently required for applications in scenarios with limited computing resources, such as drones or embedded systems. A stra…
A Unified Object Counting Network with Object Occupation Prior
Shengqin Jiang, Qing Wang, Fengna Cheng +2
The counting task, which plays a fundamental role in numerous applications (e.g., crowd counting, traffic statistics), aims to predict the number of objects with various densities.…
Unsupervised Spatial-spectral Network Learning for Hyperspectral Compressive Snapshot Reconstruction
Yubao Sun, Ying Yang, Qingshan Liu +1
Hyperspectral compressive imaging takes advantage of compressive sensing theory to achieve coded aperture snapshot measurement without temporal scanning, and the entire three-dimen…
Meta-Learning with Network Pruning
Hongduan Tian, Bo Liu, Xiao-Tong Yuan +1
Meta-learning is a powerful paradigm for few-shot learning. Although with remarkable success witnessed in many applications, the existing optimization based meta-learning models wi…
Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection
Kaihua Zhang, Tengpeng Li, Shiwen Shen +3
Co-saliency detection aims to discover the common and salient foregrounds from a group of relevant images. For this task, we present a novel adaptive graph convolutional network wi…
Dual Temporal Memory Network for Efficient Video Object Segmentation
Kaihua Zhang, Long Wang, Dong Liu +3
Video Object Segmentation (VOS) is typically formulated in a semi-supervised setting. Given the ground-truth segmentation mask on the first frame, the task of VOS is to track and s…
Classification of Hyperspectral and LiDAR Data Using Coupled CNNs
Renlong Hang, Zhu Li, Pedram Ghamisi +3
In this paper, we propose an efficient and effective framework to fuse hyperspectral and Light Detection And Ranging (LiDAR) data using two coupled convolutional neural networks (C…
Video Saliency Prediction Using Enhanced Spatiotemporal Alignment Network
Jin Chen, Huihui Song, Kaihua Zhang +2
Due to a variety of motions across different frames, it is highly challenging to learn an effective spatiotemporal representation for accurate video saliency prediction (VSP). To a…
Deep Object Co-segmentation via Spatial-Semantic Network Modulation
Kaihua Zhang, Jin Chen, Bo Liu +1
Object co-segmentation is to segment the shared objects in multiple relevant images, which has numerous applications in computer vision. This paper presents a spatial and semantic…
Cascaded Recurrent Neural Networks for Hyperspectral Image Classification
Renlong Hang, Qingshan Liu, Danfeng Hong +1
By considering the spectral signature as a sequence, recurrent neural networks (RNNs) have been successfully used to learn discriminative features from hyperspectral images (HSIs)…
Matrix Completion with Deterministic Sampling: Theories and Methods
Guangcan Liu, Qingshan Liu, Xiao-Tong Yuan +1
In some significant applications such as data forecasting, the locations of missing entries cannot obey any non-degenerate distributions, questioning the validity of the prevalent…
Robust Subspace Clustering with Compressed Data
Guangcan Liu, Zhao Zhang, Qingshan Liu +1
Dimension reduction is widely regarded as an effective way for decreasing the computation, storage and communication loads of data-driven intelligent systems, leading to a growing…
Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification
Qingshan Liu, Feng Zhou, Renlong Hang +1
This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial feature…
Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximization
Bo Liu, Xiao-Tong Yuan, Lezi Wang +2
Iterative Hard Thresholding (IHT) is a class of projected gradient descent methods for optimizing sparsity-constrained minimization models, with the best known efficiency and scala…
Unsupervised Video Segmentation via Spatio-Temporally Nonlocal Appearance Learning
Kaihua Zhang, Xuejun Li, Qingshan Liu
Video object segmentation is challenging due to the factors like rapidly fast motion, cluttered backgrounds, arbitrary object appearance variation and shape deformation. Most exist…
Learning Multi-Scale Deep Features for High-Resolution Satellite Image Classification
Qingshan Liu, Renlong Hang, Huihui Song +1
In this paper, we propose a multi-scale deep feature learning method for high-resolution satellite image classification. Specifically, we firstly warp the original satellite image…
Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification
Qingshan Liu, Renlong Hang, Huihui Song +3
Convolutional neural networks (CNNs) have attracted increasing attention in the remote sensing community. Most CNNs only take the last fully-connected layers as features for the cl…
Visual Tracking via Boolean Map Representations
Kaihua Zhang, Qingshan Liu, Ming-Hsuan Yang
In this paper, we present a simple yet effective Boolean map based representation that exploits connectivity cues for visual tracking. We describe a target object with histogram of…
A Self-Paced Regularization Framework for Multi-Label Learning
Changsheng Li, Fan Wei, Junchi Yan +4
In this paper, we propose a novel multi-label learning framework, called Multi-Label Self-Paced Learning (MLSPL), in an attempt to incorporate the self-paced learning strategy into…
Self-Paced Multi-Task Learning
Changsheng Li, Junchi Yan, Fan Wei +3
In this paper, we propose a novel multi-task learning (MTL) framework, called Self-Paced Multi-Task Learning (SPMTL). Different from previous works treating all tasks and instances…
Graph Regularized Low Rank Representation for Aerosol Optical Depth Retrieval
Yubao Sun, Renlong Hang, Qingshan Liu +2
In this paper, we propose a novel data-driven regression model for aerosol optical depth (AOD) retrieval. First, we adopt a low rank representation (LRR) model to learn a powerful…
Elastic Net Hypergraph Learning for Image Clustering and Semi-supervised Classification
Qingshan Liu, Yubao Sun, Cantian Wang +2
Graph model is emerging as a very effective tool for learning the complex structures and relationships hidden in data. Generally, the critical purpose of graph-oriented learning al…
Adaptive Compressive Tracking via Online Vector Boosting Feature Selection
Qingshan Liu, Jing Yang, Kaihua Zhang +1
Recently, the compressive tracking (CT) method has attracted much attention due to its high efficiency, but it cannot well deal with the large scale target appearance variations du…
Joint Active Learning with Feature Selection via CUR Matrix Decomposition
Changsheng Li, Xiangfeng Wang, Weishan Dong +3
This paper presents an unsupervised learning approach for simultaneous sample and feature selection, which is in contrast to existing works which mainly tackle these two problems s…