Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Self-Supervised Pyramid Representation Learning for Multi-Label Visual Analysis and Beyond
While self-supervised learning has been shown to benefit a number of vision tasks, existing techniques mainly focus on image-level manipulation, which may not generalize well to downstream tasks at patch or pixel levels.…
Instance SegmentationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONobject-detection+4PercentMatch: Percentile-based Dynamic Thresholding for Multi-Label Semi-Supervised Classification
While much of recent study in semi-supervised learning (SSL) has achieved strong performance on single-label classification problems, an equally important yet underexplored problem is how to leverage the advantage of unl…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONK-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment
Online hate speech detection has become an important issue due to the growth of online content, but resources in languages other than English are extremely limited. We introduce K-MHaS, a new multi-label dataset for hate…
Hate Speech DetectionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONFlexER: Flexible Entity Resolution for Multiple Intents
Entity resolution, a longstanding problem of data cleaning and integration, aims at identifying data records that represent the same real-world entity. Existing approaches treat entity resolution as a universal task, ass…
Entity ResolutionGraph Neural NetworkMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification
Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, given the fact that it could be expensive in practice to annotate all labels…
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Trigger-free Event Detection via Derangement Reading Comprehension
Event detection (ED), aiming to detect events from texts and categorize them, is vital to understanding actual happenings in real life. However, mainstream event detection models require high-quality expert human annotat…
Event DetectionMachine Reading ComprehensionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Open Vocabulary Multi-Label Classification with Dual-Modal Decoder on Aligned Visual-Textual Features
In computer vision, multi-label recognition are important tasks with many real-world applications, but classifying previously unseen labels remains a significant challenge. In this paper, we propose a novel algorithm, Al…
ClassificationDecoderMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Data-Efficient Vision Transformers for Multi-Label Disease Classification on Chest Radiographs
Radiographs are a versatile diagnostic tool for the detection and assessment of pathologies, for treatment planning or for navigation and localization purposes in clinical interventions. However, their interpretation and…
DiagnosticMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoring
Continual Learning aims to learn from a stream of tasks, being able to remember at the same time both new and old tasks. While many approaches were proposed for single-class classification, multi-label classification in …
Classificationclass-incremental learningClass Incremental LearningContinual Learning+3ZLPR: A Novel Loss for Multi-label Classification
In the era of deep learning, loss functions determine the range of tasks available to models and algorithms. To support the application of deep learning in multi-label classification (MLC) tasks, we propose the ZLPR (zer…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONHiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding
There are several opportunities for automation in healthcare that can improve clinician throughput. One such example is assistive tools to document diagnosis codes when clinicians write notes. We study the automation of …
Medical Code PredictionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONBEIKE NLP at SemEval-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language Detection
PCL detection task is aimed at identifying and categorizing language that is patronizing or condescending towards vulnerable communities in the general media.Compared to other NLP tasks of paragraph classification, the n…
Binary Condescension DetectionClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Large-Scale Product Retrieval with Weakly Supervised Representation Learning
Large-scale weakly supervised product retrieval is a practically useful yet computationally challenging problem. This paper introduces a novel solution for the eBay Visual Search Challenge (eProduct) held at the Ninth Wo…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRepresentation LearningRe-Ranking+1A knee cannot have lung disease: out-of-distribution detection with in-distribution voting using the medical example of chest X-ray classification
To investigate the impact of OOD radiographs on existing chest X-ray classification models and to increase their robustness against OOD data. The study employed the commonly used chest X-ray classification model, CheXnet…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONOut-of-Distribution Detection+2On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classification
The development of accurate methods for multi-label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. To address MLC problems, the use of deep neural networks that req…
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification
The propensity model introduced by Jain et al. 2016 has become a standard approach for dealing with missing and long-tail labels in extreme multi-label classification (XMLC). In this paper, we critically revise this appr…
Extreme Multi-Label ClassificationMissing LabelsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Semi-supervised Predictive Clustering Trees for (Hierarchical) Multi-label Classification
Semi-supervised learning (SSL) is a common approach to learning predictive models using not only labeled examples, but also unlabeled examples. While SSL for the simple tasks of classification and regression has received…
ClassificationClusteringDescriptiveEnsemble Learning+3FLAIR: Federated Learning Annotated Image Repository
Cross-device federated learning is an emerging machine learning (ML) paradigm where a large population of devices collectively train an ML model while the data remains on the devices. This research field has a unique set…
Federated LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONClass-Incremental Lifelong Learning in Multi-Label Classification
Existing class-incremental lifelong learning studies only the data is with single-label, which limits its adaptation to multi-label data. This paper studies Lifelong Multi-Label (LML) classification, which builds an onli…
ClassificationLifelong learningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONDiscriminative Kernel Convolution Network for Multi-Label Ophthalmic Disease Detection on Imbalanced Fundus Image Dataset
It is feasible to recognize the presence and seriousness of eye disease by investigating the progressions in retinal biological structure. Fundus examination is a diagnostic procedure to examine the biological structure …
DiagnosticMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION