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Graph-Laplacian PCA: Closed-Form Solution and Robustness
A graph-Laplacian PCA (gLPCA) to learn a low dimensional representation of X that incorporates graph structures encoded in W that is capable to remove corruptions and shows promising results on image reconstruction and significant improvement on clustering and classification. Expand
RGB-T Object Tracking: Benchmark and Baseline
A novel graph-based approach to learn a robust object representation for RGB-T tracking is proposed, in which the tracked object is represented with a graph with image patches as nodes, dynamically learned in a unified ADMM (alternating direction method of multipliers)-based optimization framework. Expand
Learning Collaborative Sparse Representation for Grayscale-Thermal Tracking
An adaptive fusion scheme is proposed based on collaborative sparse representation in Bayesian filtering framework and jointly optimize sparse codes and the reliable weights of different modalities in an online way to perform robust object tracking in challenging scenarios. Expand
Semi-Supervised Learning With Graph Learning-Convolutional Networks
The aim of GLCN is to learn an optimal graph structure that best serves graph CNNs for semi-supervised learning by integrating both graph learning and graph convolution in a unified network architecture. Expand
Weighted Sparse Representation Regularized Graph Learning for RGB-T Object Tracking
A novel graph model, called weighted sparse representation regularized graph, is proposed to learn a robust object representation using multispectral (RGB and thermal) data for visual tracking, in which the modality weight is introduced to leverage RGB and thermal information adaptively. Expand
Multi-Adapter RGBT Tracking
A novel Multi-Adapter convolutional Network (MANet) is proposed to jointly perform modality-shared, modalities-specific and instance-aware feature learning in an end-to-end trained deep framework for RGBT tracking. Expand
Weighted Low-Rank Decomposition for Robust Grayscale-Thermal Foreground Detection
This paper investigates how to fuse grayscale and thermal video data for detecting foreground objects in challenging scenarios. To this end, we propose an intuitive yet effective method calledExpand
Image Representation and Learning With Graph-Laplacian Tucker Tensor Decomposition
A graph-Laplacian tucker tensor decomposition (GLTD) which explores both attributes and pairwise similarity information simultaneously simultaneously is proposed and shown experimentally to provide a stable/unique solution to the GLTD problem. Expand
Attributes Guided Feature Learning for Vehicle Re-identification
A novel deep network architecture, which guided by meaningful attributes including camera views, vehicle types and colors for vehicle Re-ID, and design a view-specified generative adversarial network to generate the multi-view vehicle images. Expand
Grayscale-Thermal Object Tracking via Multitask Laplacian Sparse Representation
Experiments suggest that the proposedgrayscale-thermal object tracking method in Bayesian filtering framework based on multitask Laplacian sparse representation outperforms both grayscale and graysscale-Thermal tracking approaches. Expand