Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Multi-target prediction for dummies using two-branch neural networks
Multi-target prediction (MTP) serves as an umbrella term for machine learning tasks that concern the simultaneous prediction of multiple target variables. Classical instantiations are multi-label classification, multivar…
BIG-bench Machine LearningMatrix CompletionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+5WHOSe Heritage: Classification of UNESCO World Heritage "Outstanding Universal Value" Documents with Soft Labels
The UNESCO World Heritage List (WHL) includes the exceptionally valuable cultural and natural heritage to be preserved for mankind. Evaluating and justifying the Outstanding Universal Value (OUV) is essential for each si…
General ClassificationManagementMulti-Label ClassificationEnd-to-end speaker segmentation for overlap-aware resegmentation
Speaker segmentation consists in partitioning a conversation between one or more speakers into speaker turns. Usually addressed as the late combination of three sub-tasks (voice activity detection, speaker change detecti…
Action DetectionActivity DetectionChange DetectionMulti-Label Classification+4Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing
Although deep neural networks hold the state-of-the-art in several remote sensing tasks, their black-box operation hinders the understanding of their decisions, concealing any bias and other shortcomings in datasets and …
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONUnsupervised Domain Adaptation with Global and Local Graph Neural Networks in Limited Labeled Data Scenario: Application to Disaster Management
Identification and categorization of social media posts generated during disasters are crucial to reduce the sufferings of the affected people. However, lack of labeled data is a significant bottleneck in learning an eff…
Domain AdaptationGraph Neural NetworkManagementMulti-Label Classification+2Choice-Aware User Engagement Modeling andOptimization on Social Media
We address the problem of maximizing user engagement with content (in the form of like, reply, retweet, and retweet with comments)on the Twitter platform. We formulate the engagement forecasting task as a multi-label cla…
ClusteringMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAutomatically Cataloging Scholarly Articles using Library of Congress Subject Headings
Institutes are required to catalog their articles with proper subject headings so that the users can easily retrieve relevant articles from the institutional repositories. However, due to the rate of proliferation of the…
ArticlesMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONFraming Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration
Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem in which one is asked to choose only the best-fitting sense for a target word, given its context. However, gold data labelled …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONWord Sense DisambiguationLanguage that Captivates the Audience: Predicting Affective Ratings of TED Talks in a Multi-Label Classification Task
The aim of the paper is twofold: (1) to automatically predict the ratings assigned by viewers to 14 categories available for TED talks in a multi-label classification task and (2) to determine what types of features driv…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONClassifying Video based on Automatic Content Detection Overview
Video classification and analysis is always a popular and challenging field in computer vision. It is more than just simple image classification due to the correlation with respect to the semantic contents of subsequent …
ClassificationGeneral Classificationimage-classificationImage Classification+3Multi-Label Classification Neural Networks with Hard Logical Constraints
Multi-label classification (MC) is a standard machine learning problem in which a data point can be associated with a set of classes. A more challenging scenario is given by hierarchical multi-label classification (HMC) …
ClassificationGeneral ClassificationHierarchical Multi-label ClassificationMulti-Label Classification+1PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT
This study provides an efficient approach for using text data to calculate patent-to-patent (p2p) technological similarity, and presents a hybrid framework for leveraging the resulting p2p similarity for applications suc…
ClassificationGeneral ClassificationMulti-Label ClassificationPatent classification+5Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark
Perhaps surprisingly sewerage infrastructure is one of the most costly infrastructures in modern society. Sewer pipes are manually inspected to determine whether the pipes are defective. However, this process is limited …
ClassificationGeneral ClassificationImage ClassificationMulti-Label Classification+2IRLI: Iterative Re-partitioning for Learning to Index
Neural models have transformed the fundamental information retrieval problem of mapping a query to a giant set of items. However, the need for efficient and low latency inference forces the community to reconsider effici…
GPUInformation RetrievalMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1A Relational-learning Perspective to Multi-label Chest X-ray Classification
Multi-label classification of chest X-ray images is frequently performed using discriminative approaches, i.e. learning to map an image directly to its binary labels. Such approaches make it challenging to incorporate au…
ClassificationGeneral ClassificationLink PredictionMulti-Label Classification+4HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoders
Understanding how environmental characteristics affect bio-diversity patterns, from individual species to communities of species, is critical for mitigating effects of global change. A central goal for conservation plann…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSKILLBERT: “SKILLING” THE BERT TO CLASSIFY SKILLS!
In the age of digital recruitment, job posts can attract a large number of applications, and screening them manually can become a very tedious task. We propose a BERT-based model, SkillBERT, the embeddings of which are u…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONNVUM: Non-Volatile Unbiased Memory for Robust Medical Image Classification
Real-world large-scale medical image analysis (MIA) datasets have three challenges: 1) they contain noisy-labelled samples that affect training convergence and generalisation, 2) they usually have an imbalanced distribut…
image-classificationImage Classification with Label NoiseLearning with noisy labelsMedical Image Analysis+1Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification
The training of deep learning models generally requires a large amount of annotated data for effective convergence and generalisation. However, obtaining high-quality annotations is a laboursome and expensive process due…
Contrastive LearningGeneral ClassificationMedical Image AnalysisMulti-class Classification+4Stratified Sampling for Extreme Multi-Label Data
Extreme multi-label classification (XML) is becoming increasingly relevant in the era of big data. Yet, there is no method for effectively generating stratified partitions of XML datasets. Instead, researchers typically …
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION