Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
BanTH: A Multi-label Hate Speech Detection Dataset for Transliterated Bangla
The proliferation of transliterated texts in digital spaces has emphasized the need for detecting and classifying hate speech in languages beyond English, particularly in low-resource languages. As online discourse can p…
ClassificationHate Speech DetectionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Exploring space efficiency in a tree-based linear model for extreme multi-label classification
Extreme multi-label classification (XMC) aims to identify relevant subsets from numerous labels. Among the various approaches for XMC, tree-based linear models are effective due to their superior efficiency and simplicit…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text Classification+3Enhancing Hyperspectral Image Prediction with Contrastive Learning in Low-Label Regime
Self-supervised contrastive learning is an effective approach for addressing the challenge of limited labelled data. This study builds upon the previously established two-stage patch-level, multi-label classification met…
Contrastive LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONWhen the Small-Loss Trick is Not Enough: Multi-Label Image Classification with Noisy Labels Applied to CCTV Sewer Inspections
The maintenance of sewerage networks, with their millions of kilometers of pipe, heavily relies on efficient Closed-Circuit Television (CCTV) inspections. Many promising approaches based on multi-label image classificati…
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many Classes
Scientific document classification is a critical task and often involves many classes. However, collecting human-labeled data for many classes is expensive and usually leads to label-scarce scenarios. Moreover, recent wo…
ArticlesClassificationDocument ClassificationFew-Shot Learning+5Causal Micro-Narratives
We present a novel approach to classify causal micro-narratives from text. These narratives are sentence-level explanations of the cause(s) and/or effect(s) of a target subject. The approach requires only a subject-speci…
ArticlesMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSentenceStill Not Quite There! Evaluating Large Language Models for Comorbid Mental Health Diagnosis
In this study, we introduce ANGST, a novel, first-of-its kind benchmark for depression-anxiety comorbidity classification from social media posts. Unlike contemporary datasets that often oversimplify the intricate interp…
ClassificationDiagnosticMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONImproving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss Function
In multi-label emotion classification, particularly for low-resource languages like Arabic, the challenges of class imbalance and label correlation hinder model performance, especially in accurately predicting minority e…
ClassificationContrastive LearningEmotion ClassificationMeta-Learning+2Online Multi-Label Classification under Noisy and Changing Label Distribution
Multi-label data stream usually contains noisy labels in the real-world applications, namely occuring in both relevant and irrelevant labels. However, existing online multi-label classification methods are mostly limited…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA Hitchhikers Guide to Fine-Grained Face Forgery Detection Using Common Sense Reasoning
Explainability in artificial intelligence is crucial for restoring trust, particularly in areas like face forgery detection, where viewers often struggle to distinguish between real and fabricated content. Vision and Lar…
Common Sense ReasoningDeepFake DetectionFace SwappingMulti-Label Classification+5Suicide Phenotyping from Clinical Notes in Safety-Net Psychiatric Hospital Using Multi-Label Classification with Pre-Trained Language Models
Accurate identification and categorization of suicidal events can yield better suicide precautions, reducing operational burden, and improving care quality in high-acuity psychiatric settings. Pre-trained language models…
ClassificationModel OptimizationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONDeep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity Recognition
Human Activity Recognition (HAR) is a challenging, multi-label classification problem as activities may co-occur and sensor signals corresponding to the same activity may vary in different contexts (e.g., different devic…
Activity RecognitionGraph LearningHuman Activity RecognitionMulti-Label Classification+1Showing Many Labels in Multi-label Classification Models: An Empirical Study of Adversarial Examples
With the rapid development of Deep Neural Networks (DNNs), they have been applied in numerous fields. However, research indicates that DNNs are susceptible to adversarial examples, and this is equally true in the multi-l…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONExamining the Rat in the Tunnel: Interpretable Multi-Label Classification of Tor-based Malware
Despite being the most popular privacy-enhancing network, Tor is increasingly adopted by cybercriminals to obfuscate malicious traffic, hindering the identification of malware-related communications between compromised d…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA Novel Perspective for Multi-modal Multi-label Skin Lesion Classification
The efficacy of deep learning-based Computer-Aided Diagnosis (CAD) methods for skin diseases relies on analyzing multiple data modalities (i.e., clinical+dermoscopic images, and patient metadata) and addressing the chall…
DiagnosticLesion ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio Tagging
We introduce Label-Combination Prototypical Networks (LC-Protonets) to address the problem of multi-label few-shot classification, where a model must generalize to new classes based on only a few available examples. Exte…
Audio TaggingFew-Shot LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA Simpler Alternative to Variational Regularized Counterfactual Risk Minimization
Variance regularized counterfactual risk minimization (VRCRM) has been proposed as an alternative off-policy learning (OPL) method. VRCRM method uses a lower-bound on the $f$-divergence between the logging policy and the…
counterfactualMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAcoustic identification of individual animals with hierarchical contrastive learning
Acoustic identification of individual animals (AIID) is closely related to audio-based species classification but requires a finer level of detail to distinguish between individual animals within the same species. In thi…
ClassificationContrastive LearningHierarchical Multi-label ClassificationMulti-Label Classification+2Task-Augmented Cross-View Imputation Network for Partial Multi-View Incomplete Multi-Label Classification
In real-world scenarios, multi-view multi-label learning often encounters the challenge of incomplete training data due to limitations in data collection and unreliable annotation processes. The absence of multi-view fea…
ImputationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningConformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraints
This paper presents communication-constrained distributed conformal risk control (CD-CRC) framework, a novel decision-making framework for sensor networks under communication constraints. Targeting multi-label classifica…
Decision MakingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION