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Ensemble multi-view feature set partitioning method for effective multi-view learning
Multi-view learning consistently outperforms traditional single-view learning by leveraging multiple perspectives of data. However, the effectiveness...
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Numerical joint invariant level set formulation with unique image segmentation result
The level set method is one of the most widely used and powerful techniques in image science such as image/motion segmentation, object tracking, etc....
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Model architecture level privacy leakage in neural networks
Privacy leakage is one of the most critical issues in machine learning and has attracted growing interest for tasks such as demonstrating potential...
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Automated methods for diagnosis of Parkinson’s disease and predicting severity level
The recent advancements in information technology and bioinformatics have led to exceptional contributions in medical sciences. Extensive...
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Visual Out-of-Distribution Detection in Open-Set Noisy Environments
The presence of noisy examples in the training set inevitably hampers the performance of out-of-distribution (OOD) detection. In this paper, we...
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Open Set Recognition in Real World
Open set recognition (OSR) constitutes a critical endeavor within the domain of computer vision, frequently deployed in applications, such as...
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Multi-level relation learning for cross-domain few-shot hyperspectral image classification
Cross-domain few-shot hyperspectral image classification focuses on learning prior knowledge from a large number of labeled samples from source...
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Logit prototype learning with active multimodal representation for robust open-set recognition
Robust open-set recognition (OSR) performance has become a prerequisite for pattern recognition systems in real-world applications. However, the...
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Multi-region segmentation by a single level set generalization applied to stroke CT images
This article proposes a level set approach to segment images with N regions by using a single level set function. Many works use several level set...
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Analysis of impact of balanced level on MI-based and non-MI-based feature selection methods
Advancements in high-speed computer technology play an ever-increasing role in analyzing various types and massive size data. However, handling big...
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A Position-Aware Word-Level and Clause-Level Attention Network for Emotion Cause Recognition
Emotion cause recognition is a vital task in natural language processing (NLP), which aims to identify the reason of emotion expressed in text. Both...
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TransGait: Multimodal-based gait recognition with set transformer
As a biological feature that can be recognized from a distance, gait has a wide range of applications such as crime prevention, judicial...
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Universal Model Adaptation by Style Augmented Open-set Consistency
Learning to recognize unknown target samples is of great importance for unsupervised domain adaptation (UDA). Open-set domain adaptation (OSDA) and...
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ROMOT: Referring-expression-comprehension open-set multi-object tracking
Traditional multi-object tracking is limited to tracking a predefined set of categories, whereas open-vocabulary tracking expands its capabilities to...
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Enhancement of MRI images using modified type-2 fuzzy set
One of the most challenging, interesting, and influential areas in image processing is image enhancement. Image enhancement techniques manipulate the...
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Features and Methods
In addition to features and methods used in LI, this chapter introduces the notation devised by Jauhiainen et al. (2019e) that is used throughout...
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Multi-level Feature Enhancement Method for Medical Text Detection
In recent years, Segmentation-based text detection methods have been widely applied in the field of text detection. However, when it comes to tasks...
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Jacobi set simplification for tracking topological features in time-varying scalar fields
The Jacobi set of a bivariate scalar field is the set of points where the gradients of the two constituent scalar fields align with each other. It...
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Open-set learning under covariate shift
Open-set learning deals with the testing distribution where there exist samples from the classes that are unseen during training. They aim to...
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Open-set marine object instance segmentation with prototype learning
The ocean world is full of Unknown Marine Objects (UMOs), making it difficult to deal with unknown ocean targets using the traditional instance...
