Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning
Multi-label learning is a challenging computer vision task that requires assigning multiple categories to each image. However, fully annotating large-scale datasets is often impractical due to high costs and effort, moti…
Multi-Label ClassificationMulti-Label LearningMulti-Lingual Implicit Discourse Relation Recognition with Multi-Label Hierarchical Learning
This paper introduces the first multi-lingual and multi-label classification model for implicit discourse relation recognition (IDRR). Our model, HArch, is evaluated on the recently released DiscoGeM 2.0 corpus and lever…
Multi-Label ClassificationAutomated Multi-label Classification of Eleven Retinal Diseases: A Benchmark of Modern Architectures and a Meta-Ensemble on a Large Synthetic Dataset
The development of multi-label deep learning models for retinal disease classification is often hindered by the scarcity of large, expertly annotated clinical datasets due to patient privacy concerns and high costs. The …
Multi-Label ClassificationThe Application of Transformer-Based Models for Predicting Consequences of Cyber Attacks
Cyberattacks are increasing, and securing against such threats is costing industries billions of dollars annually. Threat Modeling, that is, comprehending the consequences of these attacks, can provide critical support t…
Multi-Label ClassificationInformation Bottleneck-based Causal Attention for Multi-label Medical Image Recognition
Multi-label classification (MLC) of medical images aims to identify multiple diseases and holds significant clinical potential. A critical step is to learn class-specific features for accurate diagnosis and improved inte…
Multi-Label ClassificationMulti-Objective Instruction-Aware Representation Learning in Procedural Content Generation RL
Recent advancements in generative modeling emphasize the importance of natural language as a highly expressive and accessible modality for controlling content generation. However, existing instructed reinforcement learni…
Multi-Label ClassificationRepresentation LearningReinforcement LearningStructured Spectral Graph Learning for Anomaly Classification in 3D Chest CT Scans
With the increasing number of CT scan examinations, there is a need for automated methods such as organ segmentation, anomaly detection and report generation to assist radiologists in managing their increasing workload. …
Multi-Label ClassificationAnomaly ClassificationAnomaly DetectionGraph LearningEfficient Masked Attention Transformer for Few-Shot Classification and Segmentation
Few-shot classification and segmentation (FS-CS) focuses on jointly performing multi-label classification and multi-class segmentation using few annotated examples. Although the current state of the art (SOTA) achieves h…
Multi-Label ClassificationOnline hierarchical partitioning of the output space in extreme multi-label data stream
Mining data streams with multi-label outputs poses significant challenges due to evolving distributions, high-dimensional label spaces, sparse label occurrences, and complex label dependencies. Moreover, concept drift af…
Multi-Label ClassificationMulti-Label LearningMulti-Label Classification with Generative AI Models in Healthcare: A Case Study of Suicidality and Risk Factors
Suicide remains a pressing global health crisis, with over 720,000 deaths annually and millions more affected by suicide ideation (SI) and suicide attempts (SA). Early identification of suicidality-related factors (SrFs)…
Multi-Label ClassificationBinary ClassificationChinchunmei at SemEval-2025 Task 11: Boosting the Large Language Model's Capability of Emotion Perception using Contrastive Learning
The SemEval-2025 Task 11, Bridging the Gap in Text-Based Emotion Detection, introduces an emotion recognition challenge spanning over 28 languages. This competition encourages researchers to explore more advanced approac…
Multi-Label ClassificationContrastive LearningEmotion RecognitionPolymorph: Energy-Efficient Multi-Label Classification for Video Streams on Embedded Devices
Real-time multi-label video classification on embedded devices is constrained by limited compute and energy budgets. Yet, video streams exhibit structural properties such as label sparsity, temporal continuity, and label…
Multi-Label ClassificationVideo ClassificationConstraint-aware Learning of Probabilistic Sequential Models for Multi-Label Classification
We investigate multi-label classification involving large sets of labels, where the output labels may be known to satisfy some logical constraints. We look at an architecture in which classifiers for individual labels ar…
Multi-Label ClassificationA Hybrid Framework for Subject Analysis: Integrating Embedding-Based Regression Models with Large Language Models
Providing subject access to information resources is an essential function of any library management system. Large language models (LLMs) have been widely used in classification and summarization tasks, but their capabil…
Multi-Label ClassificationAutomated Thematic Analyses Using LLMs: Xylazine Wound Management Social Media Chatter Use Case
Background Large language models (LLMs) face challenges in inductive thematic analysis, a task requiring deep interpretive and domain-specific expertise. We evaluated the feasibility of using LLMs to replicate expert-dri…
Multi-Label ClassificationPrompt EngineeringComprehensive Evaluation of Prototype Neural Networks
Prototype models are an important method for explainable artificial intelligence (XAI) and interpretable machine learning. In this paper, we perform an in-depth analysis of a set of prominent prototype models including P…
Interpretable Machine LearningMulti-Label ClassificationSCoRE: Streamlined Corpus-based Relation Extraction using Multi-Label Contrastive Learning and Bayesian kNN
The growing demand for efficient knowledge graph (KG) enrichment leveraging external corpora has intensified interest in relation extraction (RE), particularly under low-supervision settings. To address the need for adap…
Multi-Label ClassificationContrastive LearningRelation ExtractionMulti-Label Classification Framework for Hurricane Damage Assessment
Hurricanes cause widespread destruction, resulting in diverse damage types and severities that require timely and accurate assessment for effective disaster response. While traditional single-label classification methods…
Multi-Label ClassificationAudio-3DVG: Unified Audio -- Point Cloud Fusion for 3D Visual Grounding
3D Visual Grounding (3DVG) involves localizing target objects in 3D point clouds based on natural language. While prior work has made strides using textual descriptions, leveraging spoken language-known as Audio-based 3D…
Multi-Label ClassificationRepresentation LearningSpeech RecognitionVisual GroundingPrivacy-Preserving Chest X-ray Classification in Latent Space with Homomorphically Encrypted Neural Inference
Medical imaging data contain sensitive patient information requiring strong privacy protection. Many analytical setups require data to be sent to a server for inference purposes. Homomorphic encryption (HE) provides a so…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPrivacy PreservingX-ray Classification