Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Automatic ICD-10 Code Association: A Challenging Task on French Clinical Texts
Automatically associating ICD codes with electronic health data is a well-known NLP task in medical research. NLP has evolved significantly in recent years with the emergence of pre-trained language models based on Trans…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-label classification of open-ended questions with BERT
Open-ended questions in surveys are valuable because they do not constrain the respondent's answer, thereby avoiding biases. However, answers to open-ended questions are text data which are harder to analyze. Traditional…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONBridging the Gap between Model Explanations in Partially Annotated Multi-label Classification
Due to the expensive costs of collecting labels in multi-label classification datasets, partially annotated multi-label classification has become an emerging field in computer vision. One baseline approach to this task i…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTowards Integration of Discriminability and Robustness for Document-Level Relation Extraction
Document-level relation extraction (DocRE) predicts relations for entity pairs that rely on long-range context-dependent reasoning in a document. As a typical multi-label classification problem, DocRE faces the challenge…
Contrastive LearningDocument-level Relation ExtractionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2A two-head loss function for deep Average-K classification
Average-K classification is an alternative to top-K classification in which the number of labels returned varies with the ambiguity of the input image but must average to K over all the samples. A simple method to solve …
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONVocal Bursts Valence PredictionReliable Representations Learning for Incomplete Multi-View Partial Multi-Label Classification
As a cross-topic of multi-view learning and multi-label classification, multi-view multi-label classification has gradually gained traction in recent years. The application of multi-view contrastive learning has further …
ClassificationContrastive LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Neuro-symbolic Rule Learning in Real-world Classification Tasks
Neuro-symbolic rule learning has attracted lots of attention as it offers better interpretability than pure neural models and scales better than symbolic rule learning. A recent approach named pix2rule proposes a neural …
Binary ClassificationClassificationMulti-class ClassificationMulti-Label Classification+1Prior-RadGraphFormer: A Prior-Knowledge-Enhanced Transformer for Generating Radiology Graphs from X-Rays
The extraction of structured clinical information from free-text radiology reports in the form of radiology graphs has been demonstrated to be a valuable approach for evaluating the clinical correctness of report-generat…
Decision MakingMedical Image AnalysisMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Frame-Level Multi-Label Playing Technique Detection Using Multi-Scale Network and Self-Attention Mechanism
Instrument playing technique (IPT) is a key element of musical presentation. However, most of the existing works for IPT detection only concern monophonic music signals, yet little has been done to detect IPTs in polypho…
Instrument Playing Technique DetectionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONDeep Double Incomplete Multi-view Multi-label Learning with Incomplete Labels and Missing Views
View missing and label missing are two challenging problems in the applications of multi-view multi-label classification scenery. In the past years, many efforts have been made to address the incomplete multi-view learni…
ClassificationDecoderMissing LabelsMulti-Label Classification+3Knowledge Distillation from Single to Multi Labels: an Empirical Study
Knowledge distillation (KD) has been extensively studied in single-label image classification. However, its efficacy for multi-label classification remains relatively unexplored. In this study, we firstly investigate the…
Classificationimage-classificationImage ClassificationKnowledge Distillation+2DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification
In recent years, multi-view multi-label learning has aroused extensive research enthusiasm. However, multi-view multi-label data in the real world is commonly incomplete due to the uncertain factors of data collection an…
Contrastive LearningMissing LabelsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1USAGE: A Unified Seed Area Generation Paradigm for Weakly Supervised Semantic Segmentation
Seed area generation is usually the starting point of weakly supervised semantic segmentation (WSSS). Computing the Class Activation Map (CAM) from a multi-label classification network is the de facto paradigm for seed a…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSemantic SegmentationWeakly supervised Semantic Segmentation+1Efficient Image-Text Retrieval via Keyword-Guided Pre-Screening
Under the flourishing development in performance, current image-text retrieval methods suffer from $N$-related time complexity, which hinders their application in practice. Targeting at efficiency improvement, this paper…
Image-text RetrievalMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Task Learning+2Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformers
As we all know, multi-view data is more expressive than single-view data and multi-label annotation enjoys richer supervision information than single-label, which makes multi-view multi-label learning widely applicable f…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningRepresentation LearningEndoscopy Classification Model Using Swin Transformer and Saliency Map
Endoscopy is a valuable tool for the early diagnosis of colon cancer. However, it requires the expertise of endoscopists and is a time-consuming process. In this work, we propose a new multi-label classification method, …
Classificationimage-classificationImage ClassificationMulti-Label Classification+1Extending global-local view alignment for self-supervised learning with remote sensing imagery
Since large number of high-quality remote sensing images are readily accessible, exploiting the corpus of images with less manual annotation draws increasing attention. Self-supervised models acquire general feature repr…
Change DetectionContrastive LearningImage ClassificationKnowledge Distillation+5MuLTI: Efficient Video-and-Language Understanding with Text-Guided MultiWay-Sampler and Multiple Choice Modeling
Video-and-language understanding has a variety of applications in the industry, such as video question answering, text-video retrieval, and multi-label classification. Existing video-and-language understanding methods ge…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMultiple-choiceQuestion Answering+7Learning Permutation-Invariant Embeddings for Description Logic Concepts
Concept learning deals with learning description logic concepts from a background knowledge and input examples. The goal is to learn a concept that covers all positive examples, while not covering any negative examples. …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRetrievalEvaluation of Confidence-based Ensembling in Deep Learning Image Classification
Ensembling is a successful technique to improve the performance of machine learning (ML) models. Conf-Ensemble is an adaptation to Boosting to create ensembles based on model confidence instead of model errors to better …
Binary ClassificationClassificationDeep Learningimage-classification+3