Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Optimizing Multi-Stuttered Speech Classification: Leveraging Whisper's Encoder for Efficient Parameter Reduction in Automated Assessment
The automated classification of stuttered speech has significant implications for timely assessments providing assistance to speech language pathologists. Despite notable advancements in the field, the cases in which mul…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONspeech-recognitionSpeech RecognitionAttri-Net: A Globally and Locally Inherently Interpretable Model for Multi-Label Classification Using Class-Specific Counterfactuals
Interpretability is crucial for machine learning algorithms in high-stakes medical applications. However, high-performing neural networks typically cannot explain their predictions. Post-hoc explanation methods provide a…
Clinical KnowledgeMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Classification for Implicit Discourse Relation Recognition
Discourse relations play a pivotal role in establishing coherence within textual content, uniting sentences and clauses into a cohesive narrative. The Penn Discourse Treebank (PDTB) stands as one of the most extensively …
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRelationPredicting Genetic Mutation from Whole Slide Images via Biomedical-Linguistic Knowledge Enhanced Multi-label Classification
Predicting genetic mutations from whole slide images is indispensable for cancer diagnosis. However, existing work training multiple binary classification models faces two challenges: (a) Training multiple binary classif…
Binary ClassificationGraph Representation LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2EUFCC-340K: A Faceted Hierarchical Dataset for Metadata Annotation in GLAM Collections
In this paper, we address the challenges of automatic metadata annotation in the domain of Galleries, Libraries, Archives, and Museums (GLAMs) by introducing a novel dataset, EUFCC340K, collected from the Europeana porta…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONLADI v2: Multi-label Dataset and Classifiers for Low-Altitude Disaster Imagery
ML-based computer vision models are promising tools for supporting emergency management operations following natural disasters. Arial photographs taken from small manned and unmanned aircraft can be available soon after …
ManagementMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Image ClassificationAGALE: A Graph-Aware Continual Learning Evaluation Framework
In recent years, continual learning (CL) techniques have made significant progress in learning from streaming data while preserving knowledge across sequential tasks, particularly in the realm of euclidean data. To foste…
Continual LearningGraph LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1A Comparative Study of CNN, ResNet, and Vision Transformers for Multi-Classification of Chest Diseases
Large language models, notably utilizing Transformer architectures, have emerged as powerful tools due to their scalability and ability to process large amounts of data. Dosovitskiy et al. expanded this architecture to i…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONXTrack: Multimodal Training Boosts RGB-X Video Object Trackers
Multimodal sensing has proven valuable for visual tracking, as different sensor types offer unique strengths in handling one specific challenging scene where object appearance varies. While a generalist model capable of …
Inductive BiasMixture-of-ExpertsMulti-Label ClassificationObject Tracking+3Explainable machine learning multi-label classification of Spanish legal judgements
Artificial Intelligence techniques such as Machine Learning (ML) have not been exploited to their maximum potential in the legal domain. This has been partially due to the insufficient explanations they provided about th…
ClassificationJurisprudenceLegal ReasoningMulti-Label Classification+1Transformer-based Federated Learning for Multi-Label Remote Sensing Image Classification
Federated learning (FL) aims to collaboratively learn deep learning model parameters from decentralized data archives (i.e., clients) without accessing training data on clients. However, the training data across clients …
Federated Learningimage-classificationImage ClassificationMulti-Label Classification+2OptLLM: Optimal Assignment of Queries to Large Language Models
Large Language Models (LLMs) have garnered considerable attention owing to their remarkable capabilities, leading to an increasing number of companies offering LLMs as services. Different LLMs achieve different performan…
Log ParsingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONQuestion Answering+3RET-CLIP: A Retinal Image Foundation Model Pre-trained with Clinical Diagnostic Reports
The Vision-Language Foundation model is increasingly investigated in the fields of computer vision and natural language processing, yet its exploration in ophthalmology and broader medical applications remains limited. T…
DiagnosticMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONInvestigating Persuasion Techniques in Arabic: An Empirical Study Leveraging Large Language Models
In the current era of digital communication and widespread use of social media, it is crucial to develop an understanding of persuasive techniques employed in written text. This knowledge is essential for effectively dis…
Binary ClassificationFew-Shot LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Adaptable and Reliable Text Classification using Large Language Models
Text classification is fundamental in Natural Language Processing (NLP), and the advent of Large Language Models (LLMs) has revolutionized the field. This paper introduces an adaptable and reliable text classification pa…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSentiment Analysis+2Pseudo-Prompt Generating in Pre-trained Vision-Language Models for Multi-Label Medical Image Classification
The task of medical image recognition is notably complicated by the presence of varied and multiple pathological indications, presenting a unique challenge in multi-label classification with unseen labels. This complexit…
Decoderimage-classificationImage ClassificationMedical Image Classification+7Fine-grained Speech Sentiment Analysis in Chinese Psychological Support Hotlines Based on Large-scale Pre-trained Model
Suicide and suicidal behaviors remain significant challenges for public policy and healthcare. In response, psychological support hotlines have been established worldwide to provide immediate help to individuals in menta…
Emotion RecognitionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSentiment Analysis+1Boosting Single Positive Multi-label Classification with Generalized Robust Loss
Multi-label learning (MLL) requires comprehensive multi-semantic annotations that is hard to fully obtain, thus often resulting in missing labels scenarios. In this paper, we investigate Single Positive Multi-label Learn…
Missing LabelsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningTransformer models as an efficient replacement for statistical test suites to evaluate the quality of random numbers
Random numbers are incredibly important in a variety of fields, and the need for their validation remains important for safety. A Quantum Random Number Generator (QRNG) can theoretically generate truly random numbers, ho…
Deep LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSTSUniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification
Extreme Multi-label Classification (XMC) involves predicting a subset of relevant labels from an extremely large label space, given an input query and labels with textual features. Models developed for this problem have …
Extreme Multi-Label ClassificationGPUMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION