536 citations · 1.3k across the 27 of their papers we have counts for
8 papers · 1 filter
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…