Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Retrieval-augmented Encoders for Extreme Multi-label Text Classification
Extreme multi-label classification (XMC) seeks to find relevant labels from an extremely large label collection for a given text input. To tackle such a vast label space, current state-of-the-art methods fall into two ca…
Extreme Multi-Label ClassificationMemorizationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+5Optimizing CNN Architectures for Advanced Thoracic Disease Classification
Machine learning, particularly convolutional neural networks (CNNs), has shown promise in medical image analysis, especially for thoracic disease detection using chest X-ray images. In this study, we evaluate various CNN…
Binary ClassificationClassificationImage CompressionMedical Image Analysis+3Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning
Retrosynthesis strategically plans the synthesis of a chemical target compound from simpler, readily available precursor compounds. This process is critical for synthesizing novel inorganic materials, yet traditional met…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONOut-of-Distribution GeneralizationRetrosynthesisClinically-Inspired Hierarchical Multi-Label Classification of Chest X-rays with a Penalty-Based Loss Function
In this work, we present a novel approach to multi-label chest X-ray (CXR) image classification that enhances clinical interpretability while maintaining a streamlined, single-model, single-run training pipeline. Leverag…
Hierarchical Multi-label Classificationimage-classificationImage ClassificationMulti-Label Classification+1Predicting potentially abusive clauses in Chilean terms of services with natural language processing
This study addresses the growing concern of information asymmetry in consumer contracts, exacerbated by the proliferation of online services with complex Terms of Service that are rarely even read. Even though research o…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONWASUP: Interpretable Classification with Weight-Input Alignment and Class-Discriminative SUPports Vectors
The deployment of deep learning models in critical domains necessitates a balance between high accuracy and interpretability. We introduce WASUP, an inherently interpretable neural network that provides local and global …
Decision MakingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAdvances in Set Function Learning: A Survey of Techniques and Applications
Set function learning has emerged as a crucial area in machine learning, addressing the challenge of modeling functions that take sets as inputs. Unlike traditional machine learning that involves fixed-size input vectors…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTowards Robust Multi-tab Website Fingerprinting
Website fingerprinting enables an eavesdropper to determine which websites a user is visiting over an encrypted connection. State-of-the-art website fingerprinting (WF) attacks have demonstrated effectiveness even agains…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAI Guide Dog: Egocentric Path Prediction on Smartphone
This paper presents AI Guide Dog (AIGD), a lightweight egocentric (first-person) navigation system for visually impaired users, designed for real-time deployment on smartphones. AIGD employs a vision-only multi-label cla…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPredictionGraph Contrastive Learning on Multi-label Classification for Recommendations
In business analysis, providing effective recommendations is essential for enhancing company profits. The utilization of graph-based structures, such as bipartite graphs, has gained popularity for their ability to analyz…
Contrastive LearningGraph LearningLink PredictionMulti-Label Classification+2Multi-View Factorizing and Disentangling: A Novel Framework for Incomplete Multi-View Multi-Label Classification
Multi-view multi-label classification (MvMLC) has recently garnered significant research attention due to its wide range of real-world applications. However, incompleteness in views and labels is a common challenge, ofte…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRepresentation LearningSuper-class guided Transformer for Zero-Shot Attribute Classification
Attribute classification is crucial for identifying specific characteristics within image regions. Vision-Language Models (VLMs) have been effective in zero-shot tasks by leveraging their general knowledge from large-sca…
AttributeClassificationGeneral KnowledgeMulti-Label Classification+2Interpretable Enzyme Function Prediction via Residue-Level Detection
Predicting multiple functions labeled with Enzyme Commission (EC) numbers from the enzyme sequence is of great significance but remains a challenge due to its sparse multi-label classification nature, i.e., each enzyme i…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPredictionGoogle is all you need: Semi-Supervised Transfer Learning Strategy For Light Multimodal Multi-Task Classification Model
As the volume of digital image data increases, the effectiveness of image classification intensifies. This study introduces a robust multi-label classification system designed to assign multiple labels to a single image,…
Allimage-classificationImage ClassificationMulti-Label Classification+2GoBERT: Gene Ontology Graph Informed BERT for Universal Gene Function Prediction
Exploring the functions of genes and gene products is crucial to a wide range of fields, including medical research, evolutionary biology, and environmental science. However, discovering new functions largely relies on e…
Implicit RelationsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPrediction+1Classifier-guided CLIP Distillation for Unsupervised Multi-label Classification
Multi-label classification is crucial for comprehensive image understanding, yet acquiring accurate annotations is challenging and costly. To address this, a recent study suggests exploiting unsupervised multi-label …
ClassificationLanguage ModelingLanguage ModellingMulti-Label Classification+1Dynamic Prompt Adjustment for Multi-Label Class-Incremental Learning
Significant advancements have been made in single label incremental learning (SLCIL),yet the more practical and challenging multi label class incremental learning (MLCIL) remains understudied. Recently,visual language mo…
class-incremental learningClass Incremental LearningIncremental LearningMulti-Label Classification+1Multi-label Classification using Deep Multi-order Context-aware Kernel Networks
Multi-label classification is a challenging task in pattern recognition. Many deep learning methods have been proposed and largely enhanced classification performance. However, most of the existing sophisticated methods …
Classificationimage-classificationImage ClassificationMulti-Label Classification+2Detecting anxiety and depression in dialogues: a multi-label and explainable approach
Anxiety and depression are the most common mental health issues worldwide, affecting a non-negligible part of the population. Accordingly, stakeholders, including governments' health systems, are developing new strategie…
ChatbotMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONORIGAMI: A generative transformer architecture for predictions from semi-structured data
Despite the popularity and widespread use of semi-structured data formats such as JSON, end-to-end supervised learning applied directly to such data remains underexplored. We present ORIGAMI (Object RepresentatIon via Ge…
Code ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONvalid