Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Explainable Detection of Implicit Influential Patterns in Conversations via Data Augmentation
In the era of digitalization, as individuals increasingly rely on digital platforms for communication and news consumption, various actors employ linguistic strategies to influence public perception. While models have be…
Data AugmentationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAgriPotential: A Novel Multi-Spectral and Multi-Temporal Remote Sensing Dataset for Agricultural Potentials
Remote sensing has emerged as a critical tool for large-scale Earth monitoring and land management. In this paper, we introduce AgriPotential, a novel benchmark dataset composed of Sentinel-2 satellite imagery spanning m…
ManagementMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMUDAS: Mote-scale Unsupervised Domain Adaptation in Multi-label Sound Classification
Unsupervised Domain Adaptation (UDA) is essential for adapting machine learning models to new, unlabeled environments where data distribution shifts can degrade performance. Existing UDA algorithms are designed for singl…
ClassificationDomain AdaptationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2ToxSyn-PT: A Large-Scale Synthetic Dataset for Hate Speech Detection in Portuguese
We present ToxSyn-PT, the first large-scale Portuguese corpus that enables fine-grained hate-speech classification across nine legally protected minority groups. The dataset contains 53,274 synthetic sentences equally di…
Hate Speech DetectionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSingle GPU Task Adaptation of Pathology Foundation Models for Whole Slide Image Analysis
Pathology foundation models (PFMs) have emerged as powerful tools for analyzing whole slide images (WSIs). However, adapting these pretrained PFMs for specific clinical tasks presents considerable challenges, primarily d…
GPUMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMultiple Instance Learning+1PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches
Deep learning techniques have enabled vast improvements in computer vision technologies. Nevertheless, these models are vulnerable to adversarial patch attacks which catastrophically impair performance. The physically re…
Binary ClassificationClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONEfficient Text Encoders for Labor Market Analysis
Labor market analysis relies on extracting insights from job advertisements, which provide valuable yet unstructured information on job titles and corresponding skill requirements. While state-of-the-art methods for skil…
Contrastive LearningExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1GenIC: An LLM-Based Framework for Instance Completion in Knowledge Graphs
Knowledge graph completion aims to address the gaps of knowledge bases by adding new triples that represent facts. The complexity of this task depends on how many parts of a triple are already known. Instance completion …
Knowledge Graph CompletionKnowledge GraphsLink PredictionMulti-Label Classification+2Leveraging Diffusion Models for Synthetic Data Augmentation in Protein Subcellular Localization Classification
We investigate whether synthetic images generated by diffusion models can enhance multi-label classification of protein subcellular localization. Specifically, we implement a simplified class-conditional denoising diffus…
Data AugmentationDenoisingimage-classificationImage Classification+2WXImpactBench: A Disruptive Weather Impact Understanding Benchmark for Evaluating Large Language Models
Climate change adaptation requires the understanding of disruptive weather impacts on society, where large language models (LLMs) might be applicable. However, their effectiveness is under-explored due to the difficulty …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONQuestion AnsweringJNLP at SemEval-2025 Task 11: Cross-Lingual Multi-Label Emotion Detection Using Generative Models
With the rapid advancement of global digitalization, users from different countries increasingly rely on social media for information exchange. In this context, multilingual multi-label emotion detection has emerged as a…
Emotion RecognitionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONJobHop: A Large-Scale Dataset of Career Trajectories
Understanding labor market dynamics is essential for policymakers, employers, and job seekers. However, comprehensive datasets that capture real-world career trajectories are scarce. In this paper, we introduce JobHop, a…
Decision MakingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONReproducing and Improving CheXNet: Deep Learning for Chest X-ray Disease Classification
Deep learning for radiologic image analysis is a rapidly growing field in biomedical research and is likely to become a standard practice in modern medicine. On the publicly available NIH ChestX-ray14 dataset, containing…
Multi-Label ClassificationLeveraging Multi-Task Learning for Multi-Label Power System Security Assessment
This paper introduces a novel approach to the power system security assessment using Multi-Task Learning (MTL), and reformulating the problem as a multi-label classification task. The proposed MTL framework simultaneousl…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Task LearningTransfer LearningAdaptive Thresholding for Multi-Label Classification via Global-Local Signal Fusion
Multi-label classification (MLC) requires predicting multiple labels per sample, often under heavy class imbalance and noisy conditions. Traditional approaches apply fixed thresholds or treat labels independently, overlo…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONFairPO: Robust Preference Optimization for Fair Multi-Label Learning
We propose FairPO, a novel framework designed to promote fairness in multi-label classification by directly optimizing preference signals with a group robustness perspective. In our framework, the set of labels is partit…
FairnessMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningGraphATC: advancing multilevel and multi-label anatomical therapeutic chemical classification via atom-level graph learning
The accurate categorization of compounds within the anatomical therapeutic chemical (ATC) system is fundamental for drug development and fundamental research. Although this area has garnered significant research focus fo…
Drug ATC ClassificationGraph ClassificationGraph LearningMolecular Property Prediction+2Some Optimizers are More Equal: Understanding the Role of Optimizers in Group Fairness
We study whether and how the choice of optimization algorithm can impact group fairness in deep neural networks. Through stochastic differential equation analysis of optimization dynamics in an analytically tractable set…
Facial Expression RecognitionFairnessGender ClassificationMulti-Label Classification+1Can Masked Autoencoders Also Listen to Birds?
Masked Autoencoders (MAEs) have shown competitive results in audio classification by learning rich semantic representations through an efficient self-supervised reconstruction task. However, general-purpose models fail t…
Audio ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSelf-Supervised Learning+1Leveraging Large Language Models for Multi-Class and Multi-Label Detection of Drug Use and Overdose Symptoms on Social Media
Drug overdose remains a critical global health issue, often driven by misuse of opioids, painkillers, and psychiatric medications. Traditional research methods face limitations, whereas social media offers real-time insi…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATION