Search Results for author:
Found 15 papers, 2 papers with code
Diverse Representation Embedding for Lifelong Person Re-Identification
KU strategy enhances the adaptive learning ability of learner models for new information under the adjustment model prior, and KP strategy preserves old knowledge operated by representation-level alignment and logit-level supervision in limited old task datasets while guaranteeing the adaptive learning information capacity of the LReID model.
Low-Tubal-Rank Tensor Recovery via Factorized Gradient Descent
This paper considers the problem of recovering a tensor with an underlying low-tubal-rank structure from a small number of corrupted linear measurements.
Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and Learning
This paper studies the performance of deep convolutional neural networks (DCNNs) with zero-padding in feature extraction and learning.
Review helps learn better: Temporal Supervised Knowledge Distillation
Reviewing plays an important role when learning knowledge.
Joint Counting, Detection and Re-Identification for Multi-Object Tracking
The recent trend in 2D multiple object tracking (MOT) is jointly solving detection and tracking, where object detection and appearance feature (or motion) are learned simultaneously.
CCR: Facial Image Editing with Continuity, Consistency and Reversibility
In this work, we put forward three concepts and corresponding definitions: editing continuity, consistency, and reversibility.
Effective Tensor Completion via Element-wise Weighted Low-rank Tensor Train with Overlapping Ket Augmentation
Accordingly, a novel formulation for tensor completion and an effective optimization algorithm, called as tensor completion by parallel weighted matrix factorization via tensor train (TWMac-TT), is proposed.
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization
One of the most important challenge of deep learning is to figure out relations between a feature and the depth of deep neural networks (deep nets for short) to reflect the necessity of depth.
Tensor RPCA by Bayesian CP Factorization With Complex Noise
In this paper, we propose a tensor RPCA model based on CP decomposition and model data noise by Mixture of Gaussians (MoG).
Video Desnowing and Deraining Based on Matrix Decomposition
The existing snow/rain removal methods often fail for heavy snow/rain and dynamic scene.
A General Model for Robust Tensor Factorization with Unknown Noise
We provide two versions of the algorithm with different tensor factorization operations, i. e., CP factorization and Tucker factorization.
Robust Tensor Factorization With Unknown Noise
However, real data are often corrupted by noise with an unknown distribution.
Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization
In this paper, we propose a novel approach to hyperspectral image super-resolution by modeling the global spatial-and-spectral correlation and local smoothness properties over hyperspectral images.
Video Primal Sketch: A Unified Middle-Level Representation for Video
This paper presents a middle-level video representation named Video Primal Sketch (VPS), which integrates two regimes of models: i) sparse coding model using static or moving primitives to explicitly represent moving corners, lines, feature points, etc., ii) FRAME /MRF model reproducing feature statistics extracted from input video to implicitly represent textured motion, such as water and fire.
Pixel-wise Orthogonal Decomposition for Color Illumination Invariant and Shadow-free Image
In this paper, we propose a novel, effective and fast method to obtain a color illumination invariant and shadow-free image from a single outdoor image.
