Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Graph-Ensemble Learning Model for Multi-label Skin Lesion Classification using Dermoscopy and Clinical Images
Many skin lesion analysis (SLA) methods recently focused on developing a multi-modal-based multi-label classification method due to two factors. The first is multi-modal data, i.e., clinical and dermoscopy images, which …
AttributeClassificationDiagnosticEnsemble Learning+4Co-Learning Meets Stitch-Up for Noisy Multi-label Visual Recognition
In real-world scenarios, collected and annotated data often exhibit the characteristics of multiple classes and long-tailed distribution. Additionally, label noise is inevitable in large-scale annotations and hinders the…
Learning with noisy labelsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRepresentation LearningPositive Label Is All You Need for Multi-Label Classification
Multi-label classification (MLC) faces challenges from label noise in training data due to annotating diverse semantic labels for each image. Current methods mainly target identifying and correcting label mistakes using …
AllMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONProbability Guided Loss for Long-Tailed Multi-Label Image Classification
Long-tailed learning has attracted increasing attention in very recent years. Long-tailed multi-label image classification is one subtask and remains challenging and poorly researched. In this paper, we provide a fresh p…
Classificationimage-classificationImage ClassificationLong-tail Learning+3Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers
Instead of relying on human-annotated training samples to build a classifier, weakly supervised scientific paper classification aims to classify papers only using category descriptions (e.g., category names, category-ind…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONM-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks
Acquiring properly annotated data is expensive in the medical field as it requires experts, time-consuming protocols, and rigorous validation. Active learning attempts to minimize the need for large annotated samples by …
Active LearningBrain Tumor Segmentationimage-classificationImage Classification+4Dis-AE: Multi-domain & Multi-task Generalisation on Real-World Clinical Data
Clinical data is often affected by clinically irrelevant factors such as discrepancies between measurement devices or differing processing methods between sites. In the field of machine learning (ML), these factors are k…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSample-Efficient Learning of Novel Visual Concepts
Despite the advances made in visual object recognition, state-of-the-art deep learning models struggle to effectively recognize novel objects in a few-shot setting where only a limited number of examples are provided. Un…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONObject RecognitionTop-Down Framework for Weakly-supervised Grounded Image Captioning
Weakly-supervised grounded image captioning (WSGIC) aims to generate the caption and ground (localize) predicted object words in the input image without using bounding box supervision. Recent two-stage solutions mostly a…
Image CaptioningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONObject+1Active Learning Guided Fine-Tuning for enhancing Self-Supervised Based Multi-Label Classification of Remote Sensing Images
In recent years, deep neural networks (DNNs) have been found very successful for multi-label classification (MLC) of remote sensing (RS) images. Self-supervised pre-training combined with fine-tuning on a randomly select…
Active LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONImbalanced Multi-label Classification for Business-related Text with Moderately Large Label Spaces
In this study, we compared the performance of four different methods for multi label text classification using a specific imbalanced business dataset. The four methods we evaluated were fine tuned BERT, Binary Relevance,…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text ClassificationMulti-Label Text Classification+2Multi-Label ECG Classification using Temporal Convolutional Neural Network
Automated analysis of 12-lead electrocardiogram (ECG) plays a crucial role in the early screening and management of cardiovascular diseases (CVDs). In practice, it is common to see multiple co-occurring cardiac disorders…
ClassificationECG ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONCompressed Sensing: A Discrete Optimization Approach
We study the Compressed Sensing (CS) problem, which is the problem of finding the most sparse vector that satisfies a set of linear measurements up to some numerical tolerance. We introduce an $\ell_2$ regularized formul…
compressed sensingData CompressionDenoisingImage Reconstruction+2Transformer-based Multi-Modal Learning for Multi Label Remote Sensing Image Classification
In this paper, we introduce a novel Synchronized Class Token Fusion (SCT Fusion) architecture in the framework of multi-modal multi-label classification (MLC) of remote sensing (RS) images. The proposed architecture leve…
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1More than Classification: A Unified Framework for Event Temporal Relation Extraction
Event temporal relation extraction~(ETRE) is usually formulated as a multi-label classification task, where each type of relation is simply treated as a one-hot label. This formulation ignores the meaning of relations an…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRelationRelation Extraction+1Hierarchical Verbalizer for Few-Shot Hierarchical Text Classification
Due to the complex label hierarchy and intensive labeling cost in practice, the hierarchical text classification (HTC) suffers a poor performance especially when low-resource or few-shot settings are considered. Recently…
ClassificationContrastive LearningFew-shot HTCMulti-Label Classification+2Annotating and Detecting Fine-grained Factual Errors for Dialogue Summarization
A series of datasets and models have been proposed for summaries generated for well-formatted documents such as news articles. Dialogue summaries, however, have been under explored. In this paper, we present the first da…
ArticlesDecoderMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1DialogVCS: Robust Natural Language Understanding in Dialogue System Upgrade
In the constant updates of the product dialogue systems, we need to retrain the natural language understanding (NLU) model as new data from the real users would be merged into the existent data accumulated in the last up…
Intent DetectionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONNatural Language UnderstandingHiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification
Hierarchical text classification (HTC) is a challenging subtask of multi-label classification as the labels form a complex hierarchical structure. Existing dual-encoder methods in HTC achieve weak performance gains with …
Hierarchical Multi-label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Text Classification+2Understanding Label Bias in Single Positive Multi-Label Learning
Annotating data for multi-label classification is prohibitively expensive because every category of interest must be confirmed to be present or absent. Recent work on single positive multi-label (SPML) learning shows tha…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Learning