Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification
We present AiTLAS: Benchmark Arena -- an open-source benchmark suite for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO). To this end, we present a comprehensive co…
ClassificationEarth Observationimage-classificationImage Classification+5Hierarchy exploitation to detect missing annotations on hierarchical multi-label classification
The availability of genomic data has grown exponentially in the last decade, mainly due to the development of new sequencing technologies. Based on the interactions between genes (and gene products) extracted from the in…
Hierarchical Multi-label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTransFA: Transformer-based Representation for Face Attribute Evaluation
Face attribute evaluation plays an important role in video surveillance and face analysis. Although methods based on convolution neural networks have made great progress, they inevitably only deal with one local neighbor…
AttributeMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRepresentation LearningMulti-label Classification with High-rank and High-order Label Correlations
Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix fa…
Common Sense ReasoningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONVocal Bursts Intensity PredictionActive Learning and Multi-label Classification for Ellipsis and Coreference Detection in Conversational Question-Answering
In human conversations, ellipsis and coreference are commonly occurring linguistic phenomena. Although these phenomena are a mean of making human-machine conversations more fluent and natural, only few dialogue corpora c…
Active LearningConversational Question AnsweringMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Local Multi-Label Explanations for Random Forest
Multi-label classification is a challenging task, particularly in domains where the number of labels to be predicted is large. Deep neural networks are often effective at multi-label classification of images and textual …
ClassificationDecision MakingFraud DetectionMulti-Label Classification+1Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer
Real-world recognition system often encounters the challenge of unseen labels. To identify such unseen labels, multi-label zero-shot learning (ML-ZSL) focuses on transferring knowledge by a pre-trained textual label embe…
Image-text matchingKnowledge DistillationLanguage ModellingMulti-Label Classification+7Multi-Label Retinal Disease Classification using Transformers
Early detection of retinal diseases is one of the most important means of preventing partial or permanent blindness in patients. In this research, a novel multi-label classification system is proposed for the detection o…
ClassificationDecision MakingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONDiscriminatory Label-specific Weights for Multi-label Learning with Missing Labels
Class labels in multi-label datasets are only associated with a very small fraction of the data instances leading to a class imbalance problem. There exist multi-label learning algorithms that handle the datasets’ class …
Missing LabelsMulti-Label ClassificationMulti-Label LearningThe "AI+R"-tree: An Instance-optimized R-tree
The emerging class of instance-optimized systems has shown potential to achieve high performance by specializing to a specific data and query workloads. Particularly, Machine Learning (ML) techniques have been applied su…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPsychotherapy is Not One Thing: Simultaneous Modeling of Different Therapeutic Approaches
There are many different forms of psychotherapy. Itemized inventories of psychotherapeutic interventions provide a mechanism for evaluating the quality of care received by clients and for conducting research on how psych…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSemEval-2022 Task 4: Patronizing and Condescending Language Detection
This paper presents an overview of Task 4 at SemEval-2022, which was focused on detecting Patronizing and Condescending Language (PCL) towards vulnerable communities. Two sub-tasks were considered: a binary classificatio…
Binary ClassificationBinary Condescension DetectionClassificationMulti-Label Classification+5PINGAN Omini-Sinitic at SemEval-2022 Task 4: Multi-prompt Training for Patronizing and Condescending Language Detection
This paper describes the second-placed system for subtask 2 and the ninth-placed system for subtask 1 in SemEval 2022 Task 4: Patronizing and Condescending Language Detection. We propose an ensemble of prompt training an…
Binary ClassificationClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1ASRtrans at SemEval-2022 Task 4: Ensemble of Tuned Transformer-based Models for PCL Detection
Patronizing behavior is a subtle form of bullying and when directed towards vulnerable communities, it can arise inequalities. This paper describes our system for Task 4 of SemEval-2022: Patronizing and Condescending Lan…
Data AugmentationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONFelix&Julia at SemEval-2022 Task 4: Patronizing and Condescending Language Detection
This paper describes the authors’ submission to the SemEval-2022 task 4: Patronizing and Condescending Language (PCL) Detection. The aim of the task is the detection and classification of PCL in an annotated dataset. Sub…
Binary ClassificationClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTesla at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Transformer-based Models with Data Augmentation
This paper describes our system for Task 4 of SemEval 2022: Patronizing and Condescending Language (PCL) Detection. For sub-task 1, where the objective is to classify a text as PCL or non-PCL, we use a T5 Model fine-tune…
ClassificationData AugmentationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1SSN_NLP_MLRG at SemEval-2022 Task 4: Ensemble Learning strategies to detect Patronizing and Condescending Language
In this paper, we describe our efforts at SemEval 2022 Shared Task 4 on Patronizing and Condescending Language (PCL) Detection. This is the first shared task to detect PCL which is to identify and categorize PCL language…
ClassificationEnsemble LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+4McRock at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Multi-Channel CNN, Hybrid LSTM, DistilBERT and XLNet
In this paper we propose four deep learning models for the task of detecting and classifying Patronizing and Condescending Language (PCL) using a corpus of over 13,000 annotated paragraphs in English. The task, hosted at…
Binary ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTeam LRL_NC at SemEval-2022 Task 4: Binary and Multi-label Classification of PCL using Fine-tuned Transformer-based Models
Patronizing and condescending language (PCL) can find its way into many mediums of public discourse. Presence of PCL in text can produce negative effects in the society. The challenge presented by the task emerges from t…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONGUTS at SemEval-2022 Task 4: Adversarial Training and Balancing Methods for Patronizing and Condescending Language Detection
Patronizing and Condescending Language (PCL) towards vulnerable communities in general media has been shown to have potentially harmful effects. Due to its subtlety and the good intentions behind its use, the audience is…
Binary ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION