Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
HyMAD: A Hybrid Multi-Activity Detection Approach for Border Surveillance and Monitoring
Seismic sensing has emerged as a promising solution for border surveillance and monitoring; the seismic sensors that are often buried underground are small and cannot be noticed easily, making them difficult for intruder…
Multi-Label ClassificationActivity RecognitionActivity DetectionLarge Language Models Meet Extreme Multi-label Classification: Scaling and Multi-modal Framework
Foundation models have revolutionized artificial intelligence across numerous domains, yet their transformative potential remains largely untapped in Extreme Multi-label Classification (XMC). Queries in XMC are associate…
Multi-Label ClassificationComputational EfficiencyMulti-Label LearningMitigating Spurious Correlations in Patch-wise Tumor Classification on High-Resolution Multimodal Images
Patch-wise multi-label classification provides an efficient alternative to full pixel-wise segmentation on high-resolution images, particularly when the objective is to determine the presence or absence of target objects…
Multi-Label ClassificationBinary ClassificationA Disease-Aware Dual-Stage Framework for Chest X-ray Report Generation
Radiology report generation from chest X-rays is an important task in artificial intelligence with the potential to greatly reduce radiologists' workload and shorten patient wait times. Despite recent advances, existing …
Multi-Label ClassificationContrastive LearningFuzzy Label: From Concept to Its Application in Label Learning
Label learning is a fundamental task in machine learning that aims to construct intelligent models using labeled data, encompassing traditional single-label and multi-label classification models. Traditional methods typi…
Multi-Label ClassificationMulti-Label LearningCortex AISQL: A Production SQL Engine for Unstructured Data
Snowflake's Cortex AISQL is a production SQL engine that integrates native semantic operations directly into SQL. This integration allows users to write declarative queries that combine relational operations with semanti…
Multi-Label ClassificationDivide, Cache, Conquer: Dichotomic Prompting for Efficient Multi-Label LLM-Based Classification
We introduce a method for efficient multi-label text classification with large language models (LLMs), built on reformulating classification tasks as sequences of dichotomic (yes/no) decisions. Instead of generating all …
Multi-Label Text ClassificationMulti-Label ClassificationTraining LLMs Beyond Next Token Prediction -- Filling the Mutual Information Gap
Optimizing training performance in large language models (LLMs) remains an essential challenge, particularly in improving model performance while maintaining computational costs. This work challenges the conventional app…
Multi-Label ClassificationReasoning Visual Language Model for Chest X-Ray Analysis
Vision-language models (VLMs) have shown strong promise for medical image analysis, but most remain opaque, offering predictions without the transparent, stepwise reasoning clinicians rely on. We present a framework that…
Multi-Label ClassificationReinforcement LearningMulti-Label Clinical Text Eligibility Classification and Summarization System
Clinical trials are central to medical progress because they help improve understanding of human health and the healthcare system. They play a key role in discovering new ways to detect, prevent, or treat diseases, and i…
Multi-Label ClassificationGrASP: A Generalizable Address-based Semantic Prefetcher for Scalable Transactional and Analytical Workloads
Data prefetching--loading data into the cache before it is requested--is essential for reducing I/O overhead and improving database performance. While traditional prefetchers focus on sequential patterns, recent learning…
Multi-Label ClassificationOne Size Does Not Fit All: Exploring Variable Thresholds for Distance-Based Multi-Label Text Classification
Distance-based unsupervised text classification is a method within text classification that leverages the semantic similarity between a label and a text to determine label relevance. This method provides numerous benefit…
Unsupervised Text ClassificationMulti-Label Text ClassificationMulti-Label ClassificationInformation RetrievalReconstructing 12-Lead ECG from 3-Lead ECG using Variational Autoencoder to Improve Cardiac Disease Detection of Wearable ECG Devices
Twelve-lead electrocardiograms (ECGs) are the clinical gold standard for cardiac diagnosis, providing comprehensive spatial coverage of the heart necessary to detect conditions such as myocardial infarction (MI). However…
Multi-Label ClassificationStructured Spectral Graph Representation Learning for Multi-label Abnormality Analysis from 3D CT Scans
With the growing volume of CT examinations, there is an increasing demand for automated tools such as organ segmentation, abnormality detection, and report generation to support radiologists in managing their clinical wo…
Graph Representation LearningMulti-Label ClassificationFrom Detection to Mitigation: Addressing Bias in Deep Learning Models for Chest X-Ray Diagnosis
Deep learning models have shown promise in improving diagnostic accuracy from chest X-rays, but they also risk perpetuating healthcare disparities when performance varies across demographic groups. In this work, we prese…
Multi-Label ClassificationData AugmentationActive LearningBias DetectionAdaptive Gradient Calibration for Single-Positive Multi-Label Learning in Remote Sensing Image Scene Classification
Multi-label classification (MLC) offers a more comprehensive semantic understanding of Remote Sensing (RS) imagery compared to traditional single-label classification (SLC). However, obtaining complete annotations for ML…
Multi-Label ClassificationScene ClassificationMulti-Label LearningLarge Language Model-Based Uncertainty-Adjusted Label Extraction for Artificial Intelligence Model Development in Upper Extremity Radiography
Objectives: To evaluate GPT-4o's ability to extract diagnostic labels (with uncertainty) from free-text radiology reports and to test how these labels affect multi-label image classification of musculoskeletal radiograph…
Multi-Label Image ClassificationMulti-Label ClassificationFine-grained auxiliary learning for real-world product recommendation
Product recommendation is the task of recovering the closest items to a given query within a large product corpora. Generally, one can determine if top-ranked products are related to the query by applying a similarity th…
Multi-Label ClassificationProduct RecommendationRethinking Consistent Multi-Label Classification Under Inexact Supervision
Partial multi-label learning and complementary multi-label learning are two popular weakly supervised multi-label classification paradigms that aim to alleviate the high annotation costs of collecting precisely annotated…
Multi-Label ClassificationMulti-Label LearningOverview of GeoLifeCLEF 2023: Species Composition Prediction with High Spatial Resolution at Continental Scale Using Remote Sensing
Understanding the spatio-temporal distribution of species is a cornerstone of ecology and conservation. By pairing species observations with geographic and environmental predictors, researchers can model the relationship…
Multi-Label Classification