Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Unbiased Loss Functions for Multilabel Classification with Missing Labels
This paper considers binary and multilabel classification problems in a setting where labels are missing independently and with a known rate. Missing labels are a ubiquitous phenomenon in extreme multi-label classificati…
ArticlesClassificationExtreme Multi-Label ClassificationMissing Labels+2Improved Multi-label Classification with Frequent Label-set Mining and Association
Multi-label (ML) data deals with multiple classes associated with individual samples at the same time. This leads to the co-occurrence of several classes repeatedly, which indicates some existing correlation among them. …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONJointly Modeling Aspect and Polarity for Aspect-based Sentiment Analysis in Persian Reviews
Identification of user's opinions from natural language text has become an exciting field of research due to its growing applications in the real world. The research field is known as sentiment analysis and classificatio…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Aspect Category DetectionAspect Category Polarity+4Overview of Tencent Multi-modal Ads Video Understanding Challenge
Multi-modal Ads Video Understanding Challenge is the first grand challenge aiming to comprehensively understand ads videos. Our challenge includes two tasks: video structuring in the temporal dimension and multi-modal vi…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONVideo ClassificationVideo UnderstandingExpert Knowledge-Guided Length-Variant Hierarchical Label Generation for Proposal Classification
To advance the development of science and technology, research proposals are submitted to open-court competitive programs developed by government agencies (e.g., NSF). Proposal classification is one of the most important…
ClassificationHierarchical Multi-label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-label Classification of Aircraft Heading Changes Using Neural Network to Resolve Conflicts
An aircraft conflict occurs when two or more aircraft cross at a certain distance at the same time. Specific air traffic controllers are assigned to solve such conflicts. A controller needs to consider various types of i…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONLearning with Holographic Reduced Representations
Holographic Reduced Representations (HRR) are a method for performing symbolic AI on top of real-valued vectors by associating each vector with an abstract concept, and providing mathematical operations to manipulate vec…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRetrievalLegaLMFiT: Efficient Short Legal Text Classification with LSTM Language Model Pre-Training
Large Transformer-based language models such as BERT have led to broad performance improvements on many NLP tasks. Domain-specific variants of these models have demonstrated excellent performance on a variety of speciali…
Language ModelingLanguage ModellingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+3Fast Multi-label Learning
Embedding approaches have become one of the most pervasive techniques for multi-label classification. However, the training process of embedding methods usually involves a complex quadratic or semidefinite programming pr…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningSparse Array Capon Beamformer Design Availing Deep Learning
The paper considers sparse array design for receive beamforming achieving maximum signal-to-interference plus noise ratio (MaxSINR). We develop a design approach based on supervised neural network where class labels are …
Deep LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSemi-supervised classification of radiology images with NoTeacher: A Teacher that is not Mean
Deep learning models achieve strong performance for radiology image classification, but their practical application is bottlenecked by the need for large labeled training datasets. Semi-supervised learning (SSL) approach…
Classificationimage-classificationImage ClassificationMulti-Label Classification+1DECAF: Deep Extreme Classification with Label Features
Extreme multi-label classification (XML) involves tagging a data point with its most relevant subset of labels from an extremely large label set, with several applications such as product-to-product recommendation with m…
ClassificationExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+5YNU-HPCC at SemEval-2021 Task 11: Using a BERT Model to Extract Contributions from NLP Scholarly Articles
This paper describes the system we built as the YNU-HPCC team in the SemEval-2021 Task 11: NLPContributionGraph. This task involves first identifying sentences in the given natural language processing (NLP) scholarly art…
ArticlesBinary ClassificationClassificationMulti-Label Classification+2AIMH at SemEval-2021 Task 6: Multimodal Classification Using an Ensemble of Transformer Models
This paper describes the system used by the AIMH Team to approach the SemEval Task 6. We propose an approach that relies on an architecture based on the transformer model to process multimodal content (text and images) i…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONNLyticsFKIE at SemEval-2021 Task 6: Detection of Persuasion Techniques In Texts And Images
The following system description presents our approach to the detection of persuasion techniques in texts and images. The given task has been framed as a multi-label classification problem with the different techniques s…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONCSECU-DSG at SemEval-2021 Task 6: Orchestrating Multimodal Neural Architectures for Identifying Persuasion Techniques in Texts and Images
Inscribing persuasion techniques in memes is the most impactful way to influence peoples{'} mindsets. People are more inclined to memes as they are more stimulating and convincing and hence memes are often exploited by t…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMultimodal or Text? Retrieval or BERT? Benchmarking Classifiers for the Shared Task on Hateful Memes
The Shared Task on Hateful Memes is a challenge that aims at the detection of hateful content in memes by inviting the implementation of systems that understand memes, potentially by combining image and textual informati…
BenchmarkingBinary ClassificationClassificationMulti-Label Classification+3ECLARE: Extreme Classification with Label Graph Correlations
Deep extreme classification (XC) seeks to train deep architectures that can tag a data point with its most relevant subset of labels from an extremely large label set. The core utility of XC comes from predicting labels …
ClassificationExtreme Multi-Label ClassificationGraph EmbeddingMulti-Label Classification+6Multi-Label Image Classification with Contrastive Learning
Recently, as an effective way of learning latent representations, contrastive learning has been increasingly popular and successful in various domains. The success of constrastive learning in single-label classifications…
ClassificationContrastive Learningimage-classificationImage Classification+3Query2Label: A Simple Transformer Way to Multi-Label Classification
This paper presents a simple and effective approach to solving the multi-label classification problem. The proposed approach leverages Transformer decoders to query the existence of a class label. The use of Transformer …
ClassificationDecoderMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION