Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Scalable Label Distribution Learning for Multi-Label Classification
Multi-label classification (MLC) refers to the problem of tagging a given instance with a set of relevant labels. Most existing MLC methods are based on the assumption that the correlation of two labels in each label pai…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONCategory-Wise Fine-Tuning for Image Multi-label Classification with Partial Labels
Image multi-label classification datasets are often partially labeled (for each sample, only the labels on some categories are known). One popular solution for training convolutional neural networks is treating all unkno…
Binary ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONLearning Section Weights for Multi-Label Document Classification
Multi-label document classification is a traditional task in NLP. Compared to single-label classification, each document can be assigned multiple classes. This problem is crucially important in various domains, such as t…
ArticlesClassificationDocument ClassificationMulti-Label Classification+1VALUED -- Vision and Logical Understanding Evaluation Dataset
Starting with early successes in computer vision tasks, deep learning based techniques have since overtaken state of the art approaches in a multitude of domains. However, it has been demonstrated time and again that the…
ClassificationMulti-Label ClassificationICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification
This paper focuses on the task of Extreme Multi-Label Classification (XMC) whose goal is to predict multiple labels for each instance from an extremely large label space. While existing research has primarily focused on …
Extreme Multi-Label ClassificationIn-Context LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybrid Models for Multi-Label Chest X-Ray Classification
Chest X-rays are widely used to diagnose thoracic diseases, but the lack of detailed information about these abnormalities makes it challenging to develop accurate automated diagnosis systems, which is crucial for early …
Deep LearningMulti-Label ClassificationThoracic Disease ClassificationX-ray ClassificationFederated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification
Federated learning (FL) enables the collaboration of multiple deep learning models to learn from decentralized data archives (i.e., clients) without accessing data on clients. Although FL offers ample opportunities in kn…
Federated Learningimage-classificationImage ClassificationMulti-Label Classification+2Mental Health Diagnosis in the Digital Age: Harnessing Sentiment Analysis on Social Media Platforms upon Ultra-Sparse Feature Content
Amid growing global mental health concerns, particularly among vulnerable groups, natural language processing offers a tremendous potential for early detection and intervention of people's mental disorders via analyzing …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONNavigateSentiment AnalysisGeneralized test utilities for long-tail performance in extreme multi-label classification
Extreme multi-label classification (XMLC) is the task of selecting a small subset of relevant labels from a very large set of possible labels. As such, it is characterized by long-tail labels, i.e., most labels have very…
Extreme Multi-Label ClassificationMissing LabelsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONExploring Best Practices for ECG Pre-Processing in Machine Learning
In this work we search for best practices in pre-processing of Electrocardiogram (ECG) signals in order to train better classifiers for the diagnosis of heart conditions. State of the art machine learning algorithms have…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTLMCM Network for Medical Image Hierarchical Multi-Label Classification
Medical Image Hierarchical Multi-Label Classification (MI-HMC) is of paramount importance in modern healthcare, presenting two significant challenges: data imbalance and \textit{hierarchy constraint}. Existing solutions …
ClassificationHierarchical Multi-label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Breaking the Token Barrier: Chunking and Convolution for Efficient Long Text Classification with BERT
Transformer-based models, specifically BERT, have propelled research in various NLP tasks. However, these models are limited to a maximum token limit of 512 tokens. Consequently, this makes it non-trivial to apply it in …
Binary ClassificationChunkingClassificationMulti-class Classification+4Dense Retrieval as Indirect Supervision for Large-space Decision Making
Many discriminative natural language understanding (NLU) tasks have large label spaces. Learning such a process of large-space decision making is particularly challenging due to the lack of training instances per label a…
Decision MakingEntity TypingExtreme Multi-Label Classificationintent-classification+5A Multi-Modal Multilingual Benchmark for Document Image Classification
Document image classification is different from plain-text document classification and consists of classifying a document by understanding the content and structure of documents such as forms, emails, and other such docu…
ClassificationCross-Lingual TransferDocument AIDocument Classification+8Neural Collapse in Multi-label Learning with Pick-all-label Loss
We study deep neural networks for the multi-label classification (MLab) task through the lens of neural collapse (NC). Previous works have been restricted to the multi-class classification setting and discovered a preval…
AllMulti-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2What do Deck Chairs and Sun Hats Have in Common? Uncovering Shared Properties in Large Concept Vocabularies
Concepts play a central role in many applications. This includes settings where concepts have to be modelled in the absence of sentence context. Previous work has therefore focused on distilling decontextualised concept …
Entity TypingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSentenceText2Topic: Multi-Label Text Classification System for Efficient Topic Detection in User Generated Content with Zero-Shot Capabilities
Multi-label text classification is a critical task in the industry. It helps to extract structured information from large amount of textual data. We propose Text to Topic (Text2Topic), which achieves high multi-label cla…
Decision MakingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text Classification+3Arabic Dialect Identification under Scrutiny: Limitations of Single-label Classification
Automatic Arabic Dialect Identification (ADI) of text has gained great popularity since it was introduced in the early 2010s. Multiple datasets were developed, and yearly shared tasks have been running since 2018. Howeve…
Dialect IdentificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPowerset multi-class cross entropy loss for neural speaker diarization
Since its introduction in 2019, the whole end-to-end neural diarization (EEND) line of work has been addressing speaker diarization as a frame-wise multi-label classification problem with permutation-invariant training. …
Multi-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONspeaker-diarization+1HB-net: Holistic bursting cell cluster integrated network for occluded multi-objects recognition
Within the realm of image recognition, a specific category of multi-label classification (MLC) challenges arises when objects within the visual field may occlude one another, demanding simultaneous identification of both…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATION