Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
The Right Model for the Job: An Evaluation of Legal Multi-Label Classification Baselines
Multi-Label Classification (MLC) is a common task in the legal domain, where more than one label may be assigned to a legal document. A wide range of methods can be applied, ranging from traditional ML approaches to the …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAttention-Based Recurrent Neural Network For Automatic Behavior Laying Hen Recognition
One of the interests of modern poultry farming is the vocalization of laying hens which contain very useful information on health behavior. This information is used as health and well-being indicators that help breeders …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONDeep learning enhanced mixed integer optimization: Learning to reduce model dimensionality
This work introduces a framework to address the computational complexity inherent in Mixed-Integer Programming (MIP) models by harnessing the potential of deep learning. By employing deep learning, we construct problem-s…
Bayesian OptimizationDeep LearningMulti-Label ClassificationSchedulingEVOKE: Emotion Enabled Virtual Avatar Mapping Using Optimized Knowledge Distillation
As virtual environments continue to advance, the demand for immersive and emotionally engaging experiences has grown. Addressing this demand, we introduce Emotion enabled Virtual avatar mapping using Optimized KnowledgE …
Emotion RecognitionKnowledge DistillationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONClass-Incremental Learning for Multi-Label Audio Classification
In this paper, we propose a method for class-incremental learning of potentially overlapping sounds for solving a sequence of multi-label audio classification tasks. We design an incremental learner that learns new class…
Audio ClassificationClassificationclass-incremental learningClass Incremental Learning+3JMA: a General Algorithm to Craft Nearly Optimal Targeted Adversarial Example
Most of the approaches proposed so far to craft targeted adversarial examples against Deep Learning classifiers are highly suboptimal and typically rely on increasing the likelihood of the target class, thus implicitly f…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONQuery-Based Knowledge Sharing for Open-Vocabulary Multi-Label Classification
Identifying labels that did not appear during training, known as multi-label zero-shot learning, is a non-trivial task in computer vision. To this end, recent studies have attempted to explore the multi-modal knowledge o…
Knowledge DistillationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-label zero-shot learning+1Aspect category learning and sentimental analysis using weakly supervised learning
The surge of e-commerce reviews has presented a challenge in manually annotating the vast volume of reviews to comprehend their underlying aspects and sentiments. This research focused on leveraging weakly supervised lea…
Multi-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSentiment Analysis+2Automated Clinical Coding for Outpatient Departments
Computerised clinical coding approaches aim to automate the process of assigning a set of codes to medical records. While there is active research pushing the state of the art on clinical coding for hospitalized patients…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP Without Training
Contrastive Language-Image Pre-training (CLIP) has demonstrated impressive capabilities in open-vocabulary classification. The class token in the image encoder is trained to capture the global features to distinguish dif…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSemantic Segmentation+3Decoding Concerns: Multi-label Classification of Vaccine Sentiments in Social Media
In the realm of public health, vaccination stands as the cornerstone for mitigating disease risks and controlling their proliferation. The recent COVID-19 pandemic has highlighted how vaccines play a crucial role in keep…
Language ModelingLanguage ModellingLarge Language ModelMulti-Label Classification+1Multi-Label Classification of COVID-Tweets Using Large Language Models
Vaccination is important to minimize the risk and spread of various diseases. In recent years, vaccination has been a key step in countering the COVID-19 pandemic. However, many people are skeptical about the use of vacc…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONLanguage-Guided Transformer for Federated Multi-Label Classification
Federated Learning (FL) is an emerging paradigm that enables multiple users to collaboratively train a robust model in a privacy-preserving manner without sharing their private data. Most existing approaches of FL only c…
ClassificationFederated Learningimage-classificationImage Classification+4Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise
Multi-label classification poses challenges due to imbalanced and noisy labels in training data. We propose a unified data augmentation method, named BalanceMix, to address these challenges. Our approach includes two sam…
Data AugmentationDiversityMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONHierarchical Classification of Financial Transactions Through Context-Fusion of Transformer-based Embeddings and Taxonomy-aware Attention Layer
This work proposes the Two-headed DragoNet, a Transformer-based model for hierarchical multi-label classification of financial transactions. Our model is based on a stack of Transformers encoder layers that generate cont…
Hierarchical Multi-label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONEnhancing Medical Specialty Assignment to Patients using NLP Techniques
The introduction of Large Language Models (LLMs), and the vast volume of publicly available medical data, amplified the application of NLP to the medical domain. However, LLMs are pretrained on data that are not explicit…
Language ModelingLanguage ModellingLarge Language ModelMulti-Label Classification+3Text as Image: Learning Transferable Adapter for Multi-Label Classification
Pre-trained vision-language models have notably accelerated progress of open-world concept recognition. Their impressive zero-shot ability has recently been transferred to multi-label image classification via prompt tuni…
image-classificationImage ClassificationInstruction FollowingMulti-Label Classification+3Adaptive Hinge Balance Loss for Document-Level Relation Extraction
Document-Level Relation Extraction aims at predicting relations between entities from multiple sentences. A common practice is to select multi-label classification thresholds to decide whether a relation exists between a…
Document-level Relation ExtractionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRelation+1Prompted Zero-Shot Multi-label Classification of Factual Incorrectness in Machine-Generated Summaries
This study addresses the critical issue of factual inaccuracies in machine-generated text summaries, an increasingly prevalent issue in information dissemination. Recognizing the potential of such errors to compromise in…
ArticlesMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONLong-tailed multi-label classification with noisy label of thoracic diseases from chest X-ray
Chest X-rays (CXR) often reveal rare diseases, demanding precise diagnosis. However, current computer-aided diagnosis (CAD) methods focus on common diseases, leading to inadequate detection of rare conditions due to the …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATION