Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Hierarchical Multi-Label Classification with Missing Information for Benthic Habitat Imagery
In this work, we apply state-of-the-art self-supervised learning techniques on a large dataset of seafloor imagery, \textit{BenthicNet}, and study their performance for a complex hierarchical multi-label (HML) classifica…
Hierarchical Multi-label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSelf-Supervised LearningScalable Multitask Learning Using Gradient-based Estimation of Task Affinity
Multitask learning is a widely used paradigm for training models on diverse tasks, with applications ranging from graph neural networks to language model fine-tuning. Since tasks may interfere with each other, a key noti…
GPUMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTAGGraphEx: A Graph-based Extraction Method for Advertiser Keyphrase Recommendation
Online sellers and advertisers are recommended keyphrases for their listed products, which they bid on to enhance their sales. One popular paradigm that generates such recommendations is Extreme Multi-Label Classificatio…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONEfficient and Scalable Estimation of Tool Representations in Vector Space
Recent advancements in function calling and tool use have significantly enhanced the capabilities of large language models (LLMs) by enabling them to interact with external information sources and execute complex tasks. …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRetrievalMedical Report Generation Is A Multi-label Classification Problem
Medical report generation is a critical task in healthcare that involves the automatic creation of detailed and accurate descriptions from medical images. Traditionally, this task has been approached as a sequence genera…
Medical Report GenerationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAutomatic Differential Diagnosis using Transformer-Based Multi-Label Sequence Classification
As the field of artificial intelligence progresses, assistive technologies are becoming more widely used across all industries. The healthcare industry is no different, with numerous studies being done to develop assisti…
DiagnosticMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONLegilimens: Practical and Unified Content Moderation for Large Language Model Services
Given the societal impact of unsafe content generated by large language models (LLMs), ensuring that LLM services comply with safety standards is a crucial concern for LLM service providers. Common content moderation met…
Data AugmentationLanguage ModelingLanguage ModellingLarge Language Model+2Deep Learning-Based Automatic Multi-Level Airway Collapse Monitoring on Obstructive Sleep Apnea Patients
This study investigated the use of deep learning to identify multi-level upper airway collapses in obstructive sleep apnea (OSA) patients based on snoring sounds. We fi-ne-tuned ResNet-50 and Audio Spectrogram Transforme…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONFrom Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning
Open-vocabulary Extreme Multi-label Classification (OXMC) extends traditional XMC by allowing prediction beyond an extremely large, predefined label set (typically $10^3$ to $10^{12}$ labels), addressing the dynamic natu…
Extreme Multi-Label ClassificationKeyphrase GenerationMissing LabelsMulti-Label Classification+2A Multi-Task and Multi-Label Classification Model for Implicit Discourse Relation Recognition
We address the inherent ambiguity in Implicit Discourse Relation Recognition (IDRR) by introducing a novel multi-task classification model capable of learning both multi-label and single-label representations of discours…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRelation+1Only One Relation Possible? Modeling the Ambiguity in Event Temporal Relation Extraction
Event Temporal Relation Extraction (ETRE) aims to identify the temporal relationship between two events, which plays an important role in natural language understanding. Most previous works follow a single-label classifi…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONNatural Language UnderstandingRelation+2Hierarchy-aware Biased Bound Margin Loss Function for Hierarchical Text Classification
Hierarchical text classification (HTC) is a challenging problem with two key issues: utilizing structural information and mitigating label imbalance. Recently, the unit-based approach generating unit-based feature repres…
Hierarchical Multi-label ClassificationMulti-Label Classificationtext-classificationText ClassificationRobust Domain Generalization for Multi-modal Object Recognition
In multi-label classification, machine learning encounters the challenge of domain generalization when handling tasks with distributions differing from the training data. Existing approaches primarily focus on vision obj…
Domain GeneralizationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONObject+1HiLight: A Hierarchy-aware Light Global Model with Hierarchical Local ConTrastive Learning
Hierarchical text classification (HTC) is a special sub-task of multi-label classification (MLC) whose taxonomy is constructed as a tree and each sample is assigned with at least one path in the tree. Latest HTC models c…
ClassificationContrastive LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Cost-constrained multi-label group feature selection using shadow features
We consider the problem of feature selection in multi-label classification, considering the costs assigned to groups of features. In this task, the goal is to select a subset of features that will be useful for predictin…
Diagnosticfeature selectionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONDomain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population
Objectives: This study aims to assess the impact of domain shift on chest X-ray classification accuracy and to analyze the influence of ground truth label quality and demographic factors such as age group, sex, and study…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTransfer Learning+1Multi-label Cluster Discrimination for Visual Representation Learning
Contrastive Language Image Pre-training (CLIP) has recently demonstrated success across various tasks due to superior feature representation empowered by image-text contrastive learning. However, the instance discriminat…
Contrastive LearningImage-text RetrievalMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+8Masks and Manuscripts: Advancing Medical Pre-training with End-to-End Masking and Narrative Structuring
Contemporary medical contrastive learning faces challenges from inconsistent semantics and sample pair morphology, leading to dispersed and converging semantic shifts. The variability in text reports, due to multiple aut…
Contrastive LearningMedical Image AnalysisMulti-Label ClassificationPosition+1Error Detection and Constraint Recovery in Hierarchical Multi-Label Classification without Prior Knowledge
Recent advances in Hierarchical Multi-label Classification (HMC), particularly neurosymbolic-based approaches, have demonstrated improved consistency and accuracy by enforcing constraints on a neural model during trainin…
Hierarchical Multi-label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONImproving GBDT Performance on Imbalanced Datasets: An Empirical Study of Class-Balanced Loss Functions
Class imbalance remains a significant challenge in machine learning, particularly for tabular data classification tasks. While Gradient Boosting Decision Trees (GBDT) models have proven highly effective for such tasks, t…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONtabular-classification