Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Dual-Encoders for Extreme Multi-Label Classification
Dual-encoder (DE) models are widely used in retrieval tasks, most commonly studied on open QA benchmarks that are often characterized by multi-class and limited training data. In contrast, their performance in multi-labe…
ClassificationExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+3Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label Classification
Sigmoid output layers are widely used in multi-label classification (MLC) tasks, in which multiple labels can be assigned to any input. In many practical MLC tasks, the number of possible labels is in the thousands, ofte…
Multi-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONIncorporating Domain Knowledge Graph into Multimodal Movie Genre Classification with Self-Supervised Attention and Contrastive Learning
Multimodal movie genre classification has always been regarded as a demanding multi-label classification task due to the diversity of multimodal data such as posters, plot summaries, trailers and metadata. Although exist…
Contrastive LearningGenre classificationGraph EmbeddingKnowledge Graph Embedding+3AE-smnsMLC: Multi-Label Classification with Semantic Matching and Negative Label Sampling for Product Attribute Value Extraction
Product attribute value extraction plays an important role for many real-world applications in e-Commerce such as product search and recommendation. Previous methods treat it as a sequence labeling task that needs more a…
AttributeAttribute Value ExtractionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Heuristic Vision Pre-Training with Self-Supervised and Supervised Multi-Task Learning
To mimic human vision with the way of recognizing the diverse and open world, foundation vision models are much critical. While recent techniques of self-supervised learning show the promising potentiality of this missio…
Common Sense ReasoningContrastive LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+6Large Language Models for Propaganda Detection
The prevalence of propaganda in our digital society poses a challenge to societal harmony and the dissemination of truth. Detecting propaganda through NLP in text is challenging due to subtle manipulation techniques and …
ArticlesMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPrompt Engineering+1DKEC: Domain Knowledge Enhanced Multi-Label Classification for Diagnosis Prediction
Multi-label text classification (MLTC) tasks in the medical domain often face the long-tail label distribution problem. Prior works have explored hierarchical label structures to find relevant information for few-shot cl…
ClassificationKnowledge GraphsMedical DiagnosisMulti-Label Classification+5HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Images
Chest X-ray is an essential diagnostic tool in the identification of chest diseases given its high sensitivity to pathological abnormalities in the lungs. However, image-driven diagnosis is still challenging due to heter…
DiagnosticMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSensitivityInstances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification
Hierarchical multi-label text classification (HMTC) aims at utilizing a label hierarchy in multi-label classification. Recent approaches to HMTC deal with the problem of imposing an over-constrained premise on the output…
Contrastive LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text Classification+3Balancing Efficiency vs. Effectiveness and Providing Missing Label Robustness in Multi-Label Stream Classification
Available works addressing multi-label classification in a data stream environment focus on proposing accurate models; however, these models often exhibit inefficiency and cannot balance effectiveness and efficiency. In …
Ensemble LearningImputationMissing LabelsMulti-Label Classification+1LawBench: Benchmarking Legal Knowledge of Large Language Models
Large language models (LLMs) have demonstrated strong capabilities in various aspects. However, when applying them to the highly specialized, safe-critical legal domain, it is unclear how much legal knowledge they posses…
ArticlesBenchmarkingMemorizationMulti-Label Classification+1Weakly Supervised Semantic Segmentation by Knowledge Graph Inference
Currently, existing efforts in Weakly Supervised Semantic Segmentation (WSSS) based on Convolutional Neural Networks (CNNs) have predominantly focused on enhancing the multi-label classification network stage, with limit…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSegmentation+3Prompt Tuned Embedding Classification for Multi-Label Industry Sector Allocation
Prompt Tuning is emerging as a scalable and cost-effective method to fine-tune Pretrained Language Models (PLMs), which are often referred to as Large Language Models (LLMs). This study benchmarks the performance and com…
BenchmarkingClassificationComputational EfficiencyMulti-Label Classification+5Multi-Label Takagi-Sugeno-Kang Fuzzy System
Multi-label classification can effectively identify the relevant labels of an instance from a given set of labels. However,the modeling of the relationship between the features and the labels is critical to the classific…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA New Interpretable Neural Network-Based Rule Model for Healthcare Decision Making
In healthcare applications, understanding how machine/deep learning models make decisions is crucial. In this study, we introduce a neural network framework, $\textit{Truth Table rules}$ (TT-rules), that combines the glo…
Binary ClassificationDecision MakingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAutomatic Bat Call Classification using Transformer Networks
Automatically identifying bat species from their echolocation calls is a difficult but important task for monitoring bats and the ecosystem they live in. Major challenges in automatic bat call identification are high cal…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONNeural Network-Based Rule Models With Truth Tables
Understanding the decision-making process of a machine/deep learning model is crucial, particularly in security-sensitive applications. In this study, we introduce a neural network framework that combines the global and …
Binary ClassificationDecision MakingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA Multi-label Classification Approach to Increase Expressivity of EMG-based Gesture Recognition
Objective: The objective of the study is to efficiently increase the expressivity of surface electromyography-based (sEMG) gesture recognition systems. Approach: We use a problem transformation approach, in which actions…
Gesture RecognitionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSynthetic Data GenerationRepresentation Synthesis by Probabilistic Many-Valued Logic Operation in Self-Supervised Learning
In this paper, we propose a new self-supervised learning (SSL) method for representations that enable logic operations. Representation learning has been applied to various tasks, such as image generation and retrieval. T…
Image ClassificationImage GenerationImage RetrievalMulti-Label Classification+4dacl1k: Real-World Bridge Damage Dataset Putting Open-Source Data to the Test
Recognising reinforced concrete defects (RCDs) is a crucial element for determining the structural integrity, traffic safety and durability of bridges. However, most of the existing datasets in the RCD domain are derived…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATION