9 Result(s)
within
Article
Junjun JIang
Computer Science
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Article
Overhead-free Noise-tolerant Federated Learning: A New Baseline
Federated learning (FL) is a promising decentralized machine learning approach that enables multiple distributed clients to train a model jointly while kee** their data private. However, in real-world scenar...
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Article
Open AccessDepthFormer: Exploiting Long-range Correlation and Local Information for Accurate Monocular Depth Estimation
This paper aims to address the problem of supervised monocular depth estimation. We start with a meticulous pilot study to demonstrate that the long-range correlation is essential for accurate depth estimation...
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Open AccessImage Matching from Handcrafted to Deep Features: A Survey
As a fundamental and critical task in various visual applications, image matching can identify then correspond the same or similar structure/content from two or more images. Over the past decades, growing amou...
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Noisy practical facial super-resolution method via deformable constrained model with small dataset
Face Super-Resolution (FSR) is to infer high resolution facial image(s) from given low resolution one(s). But when large-scale training samples are absent, FSR may fail in inferring high resolution image for p...
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Locality Preserving Matching
Seeking reliable correspondences between two feature sets is a fundamental and important task in computer vision. This paper attempts to remove mismatches from given putative image feature correspondences. To ...
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Action recognition from depth sequences using weighted fusion of 2D and 3D auto-correlation of gradients features
This paper presents a new framework for human action recognition from depth sequences. An effective depth feature representation is developed based on the fusion of 2D and 3D auto-correlation of gradients feat...
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HRM graph constrained dictionary learning for face image super-resolution
Sparse coding based face image Super-Resolution (SR) approaches have received increasing amount of interest recently. However, most of the existing sparse coding based approaches fail to consider the geometric...
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Heteroskedasticity tuned mixed-norm sparse regularization for face hallucination
Face hallucination is typically an ill-posed inverse problem, so it is essential to exploit an effective norm-regularized underlying representation. Due to the under-sparsity or over-sparsity, the widely used ...
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Article
Efficient single image super-resolution via graph-constrained least squares regression
We explore in this paper an efficient algorithmic solution to single image super-resolution (SR). We propose the gCLSR, namely graph-Constrained Least Squares Regression, to super-resolve a high-resolution (HR...
