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Rotation Augmented Distillation for Exemplar-Free Class Incremental Learning with Detailed Analysis
Class incremental learning (CIL) aims to recognize both the old and new classes along the increment tasks. Deep neural networks in CIL suffer from...
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Exemplar-Free Lifelong Person Re-identification via Prompt-Guided Adaptive Knowledge Consolidation
Lifelong person re-identification (LReID) refers to matching people across different cameras given continuous data streams. The challenge of...
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Learning a dual-branch classifier for class incremental learning
Catastrophic forgetting is a non-trivial challenge for class incremental learning, which is caused by new knowledge learning and data imbalance...
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ExpT: Online Action Detection via Exemplar-Enhanced Transformer for Secondary School Experimental Evaluation
Secondary school experimental evaluation is an essential component of secondary school science education. However, it faces several challenges,...
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Semantic-Sparse Colorization Network for Deep Exemplar-Based Colorization
Exemplar-based colorization approaches rely on reference image to provide plausible colors for target gray-scale image. The key and difficulty of...
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TLCE: Transfer-Learning Based Classifier Ensembles for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) struggles to incrementally recognize novel classes from few examples without catastrophic forgetting of...
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Prototype Representation Expansion in Incremental Learning
Deep neural networks have made outstanding achievements in many static tasks, however, when faced with incremental scenario, they suffer from...
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Computer-aided diagnosis of COVID-19 from chest X-ray images using histogram-oriented gradient features and Random Forest classifier
The decision-making process is very crucial in healthcare, which includes quick diagnostic methods to monitor and prevent the COVID-19 pandemic...
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Improving trust and confidence in medical skin lesion diagnosis through explainable deep learning
A key issue in critical contexts such as medical diagnosis is the interpretability of the deep learning models adopted in decision-making systems....
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Automatic Check-Out via Prototype-Based Classifier Learning from Single-Product Exemplars
Automatic Check-Out (ACO) aims to accurately predict the presence and count of each category of products in check-out images, where a major challenge...
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Non-linear Sorenson–Dice Exemplar Image Inpainting Based Bayes Probability for Occlusion Removal in Remote Traffic Control
Occlusion removal is a significant problem to be resolved in a remote traffic control system to enhance road safety. However, the conventional...
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The Collostruction-Based Definition Model in Language-Specific Chinese-English Learner’s Dictionaries: The Case of Chinese Collective Classifier ‘Bǎ’
Definitions in language-specific Chinese-English learner’s dictionaries, which target at English-speaking Chinese learners, should treat the...
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ExDarkLBP: a hybrid deep feature generation-based genetic malformation detection using facial images
Body malformations, including those affecting the face, can arise as a result of genetic disorders. The diagnosis of such changes may often require...
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Classifier and Exemplar Synthesis for Zero-Shot Learning
Zero-shot learning (ZSL) enables solving a task without the need to see its examples. In this paper, we propose two ZSL frameworks that learn to synthe...
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A Generic and Model-Agnostic Exemplar Synthetization Framework for Explainable AI
With the growing complexity of deep learning methods adopted in practical applications, there is an increasing and stringent need to explain and...
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Automated stenosis classification on invasive coronary angiography using modified dual cross pattern with iterative feature selection
Coronary artery disease (CAD) is a global health concern; the need for early diagnosis cannot be overstated. Many machine learning techniques have...
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Deep Reinforcement Exemplar Learning for Annotation Refinement
Due to the inter-observer variation, the ground truth of lesion areas in pathological images is generated by majority-voting of annotations provided...
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Explaining short text classification with diverse synthetic exemplars and counter-exemplars
We present xspells , a model-agnostic local approach for explaining the decisions of black box models in classification of short texts. The...
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Compositional Prompting for Anti-Forgetting in Domain Incremental Learning
Domain Incremental Learning (DIL) focuses on handling complex domain shifts of a continuous data stream for visual tasks such as image classification...
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A Continual Learning Approach for Cross-Domain White Blood Cell Classification
Accurate classification of white blood cells in peripheral blood is essential for diagnosing hematological diseases. Due to constantly evolving...