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- Deep Learning for Single Image Super-Resolution: A Brief Review1,154
This survey reviews representative deep learning-based SISR methods and group them into two categories according to their contributions to two essential aspects of S ISR: The exploration of efficient neural network architectures for SISS and the development of effective optimization objectives for deep SISr learning.
- GAN Inversion: A Survey653
This paper provides a survey of GAN inversion with a focus on its representative algorithms and its applications in image restoration and image manipulation, and discusses the trends and challenges for future research.
- Assimilation of remote sensing into crop growth models: Current status and perspectives497
A critique of both the advantages and disadvantages of both EO data and crop growth models is provided, and a solid and robust framework for DA is introduced, where different DA methods are shown to be derived from taking different assumptions in solving for the a posteriori probability density function using Bayes’ rule.
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- Deep metric learning for few-shot image classification: A Review of recent developments237
This paper provides an up-to-date review of deep metric learning methods for few-shot image classification from 2018 to 2022 and categorizes them into three groups according to three stages of metric learning, namely learning feature embeddings, learning class representations, and learning distance measures.
- BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification214
A so-called Bi-Similarity Network (BSNet) that consists of a single embedding module and a bi-similarity module of two similarity measures of diverse characteristics that is enabled to learn more discriminative and less similarity-biased features from few shots of fine-grained images, such that the model generalization ability can be significantly improved.
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- Spatial-Temporal Attention Res-TCN for Skeleton-Based Dynamic Hand Gesture Recognition149
An end-to-end Spatial-Temporal Attention Residual Temporal Convolutional Network (STA-Res-TCN) for skeleton-based dynamic hand gesture recognition, which learns different levels of attention and assigns them to each spatial-temporal feature extracted by the convolution filters at each time step.
- A concise review of recent few-shot meta-learning methods145
This short communication gives a concise review on recent representative methods in few-shot meta-learning, which are categorized into four branches according to their technical characteristics.
- Decorrelation of Neutral Vector Variables: Theory and Applications121
Two fundamental invertible transformations, namely, serial non linear transformation and parallel nonlinear transformation, are proposed to carry out the decorrelation, which shows that a highly negatively correlated neutral vector can be transformed to a set of mutually independent scalar variables with the same degrees of freedom.
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- Adjusting the imbalance ratio by the dimensionality of imbalanced data112
A new imbalance extent metric is proposed, the adjusted IR, by adding a penalty term of the number of discriminative features that is effectively determined by the Pearson correlation test, which demonstrates the effectiveness of theadjusted IR in terms of its better negative correlation with the classification performance.
- $t$-Tests, $F$-Tests and Otsu's Methods for Image Thresholding110
It is naturally demonstrated that the extension of Otsu's binarization method to multi-level thresholding is equivalent to the search for optimal thresholds that provide the largest F -statistic through one-way analysis of variance (ANOVA).
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- Comment on “On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes”98
Empirical and simulation studies suggest that pairing of either LDA assuming a common diagonal covariance matrix (LDA-Λ) or the naïve Bayes classifier and linear logistic regression may not be perfect, and hence it may be reliable for any claim that was derived from the comparison between LDA- Λ or the naive Bayesclassifier andlinear logistic regressors to be generalised to all generative and discriminative classifiers.
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- An α-Matte Boundary Defocus Model-Based Cascaded Network for Multi-Focus Image Fusion82
Experiments demonstrate that the proposed MMF-Net outperforms the state-of-the-art methods both qualitatively and quantitatively.
- An integrated method of adaptive enhancement for unsupervised segmentation of MRI brain images82
This paper presents an integrated method of the adaptive enhancement for an unsupervised global-to-local segmentation of brain tissues in three-dimensional MRI (Magnetic Resonance Imaging) images, and yields a robust and accurate segmentation, particularly in noisy images.
- An {\alpha}-Matte Boundary Defocus Model Based Cascaded Network for Multi-focus Image Fusion81
Experiments demonstrate that the proposed MMF-Net outperforms the state-of-the-art methods both qualitatively and quantitatively.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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