Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Looking Beyond What You See: An Empirical Analysis on Subgroup Intersectional Fairness for Multi-label Chest X-ray Classification Using Social Determinants of Racial Health Inequities
There has been significant progress in implementing deep learning models in disease diagnosis using chest X- rays. Despite these advancements, inherent biases in these models can lead to disparities in prediction accurac…
DiagnosticFairnessMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Hierarchical Multi-label Classification for Fine-level Event Extraction from Aviation Accident Reports
A large volume of accident reports is recorded in the aviation domain, which greatly values improving aviation safety. To better use those reports, we need to understand the most important events or impact factors accord…
Event ExtractionHierarchical Multi-label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONDetermined Multi-Label Learning via Similarity-Based Prompt
In multi-label classification, each training instance is associated with multiple class labels simultaneously. Unfortunately, collecting the fully precise class labels for each training instance is time- and labor-consum…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningPrompt LearningRanking Distillation for Open-Ended Video Question Answering with Insufficient Labels
This paper focuses on open-ended video question answering, which aims to find the correct answers from a large answer set in response to a video-related question. This is essentially a multi-label classification task, si…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONQuestion AnsweringVideo Question AnsweringBirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics
Deep learning (DL) has greatly advanced audio classification, yet the field is limited by the scarcity of large-scale benchmark datasets that have propelled progress in other domains. While AudioSet is a pivotal step to …
Audio ClassificationClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds
Multi-label imbalanced classification poses a significant challenge in machine learning, particularly evident in bioacoustics where animal sounds often co-occur, and certain sounds are much less frequent than others. Thi…
imbalanced classificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONDeep learning for multi-label classification of coral conditions in the Indo-Pacific via underwater photogrammetry
Since coral reef ecosystems face threats from human activities and climate change, coral conservation programs are implemented worldwide. Monitoring coral health provides references for guiding conservation activities. H…
Decision MakingEnsemble LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRethinking Transformers Pre-training for Multi-Spectral Satellite Imagery
Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks by pre-training on large amount of unlabelled data. Such pre-training techniq…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONNeural Field Classifiers via Target Encoding and Classification Loss
Neural field methods have seen great progress in various long-standing tasks in computer vision and computer graphics, including novel view synthesis and geometry reconstruction. As existing neural field methods try to p…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONNeRF+2Embedded Multi-label Feature Selection via Orthogonal Regression
In the last decade, embedded multi-label feature selection methods, incorporating the search for feature subsets into model optimization, have attracted considerable attention in accurately evaluating the importance of f…
feature selectionModel OptimizationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Utilizing Local Hierarchy with Adversarial Training for Hierarchical Text Classification
Hierarchical text classification (HTC) is a challenging subtask of multi-label classification due to its complex taxonomic structure. Nearly all recent HTC works focus on how the labels are structured but ignore the sub-…
AllMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONtext-classification+1Automated Machine Learning for Multi-Label Classification
Automated machine learning (AutoML) aims to select and configure machine learning algorithms and combine them into machine learning pipelines tailored to a dataset at hand. For supervised learning tasks, most notably bin…
AutoMLClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONImproving Neural-based Classification with Logical Background Knowledge
Neurosymbolic AI is a growing field of research aiming to combine neural networks learning capabilities with the reasoning abilities of symbolic systems. This hybridization can take many shapes. In this paper, we propose…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTowards Improved Imbalance Robustness in Continual Multi-Label Learning with Dual Output Spiking Architecture (DOSA)
Algorithms designed for addressing typical supervised classification problems can only learn from a fixed set of samples and labels, making them unsuitable for the real world, where data arrives as a stream of samples of…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningWhat limits performance of weakly supervised deep learning for chest CT classification?
Weakly supervised learning with noisy data has drawn attention in the medical imaging community due to the sparsity of high-quality disease labels. However, little is known about the limitations of such weakly supervised…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONWeakly-supervised LearningHierarchical Multi-Label Classification of Online Vaccine Concerns
Vaccine concerns are an ever-evolving target, and can shift quickly as seen during the COVID-19 pandemic. Identifying longitudinal trends in vaccine concerns and misinformation might inform the healthcare space by helpin…
ClassificationHierarchical Multi-label ClassificationMisinformationMulti-Label Classification+1Deep Learning for Multi-Label Learning: A Comprehensive Survey
Multi-label learning is a rapidly growing research area that aims to predict multiple labels from a single input data point. In the era of big data, tasks involving multi-label classification (MLC) or ranking present sig…
Deep LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Learning+1Consistent algorithms for multi-label classification with macro-at-$k$ metrics
We consider the optimization of complex performance metrics in multi-label classification under the population utility framework. We mainly focus on metrics linearly decomposable into a sum of binary classification utili…
Binary ClassificationClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA Comparative Analysis of Noise Reduction Methods in Sentiment Analysis on Noisy Bangla Texts
While Bangla is considered a language with limited resources, sentiment analysis has been a subject of extensive research in the literature. Nevertheless, there is a scarcity of exploration into sentiment analysis specif…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSentiment AnalysisIn-Context Learning for Extreme Multi-Label Classification
Multi-label classification problems with thousands of classes are hard to solve with in-context learning alone, as language models (LMs) might lack prior knowledge about the precise classes or how to assign them, and it …
ClassificationExtreme Multi-Label ClassificationIn-Context LearningMulti-Label Classification+2