Papers Multi-Label Learning
“Multi-Label Learning” 태그가 달린 논문 334편 · 필터 해제
Shape-preserving Tooth Segmentation from CBCT Images Using Deep Learning with Semantic and Shape Awareness
Background:Accurate tooth segmentation from cone beam computed tomography (CBCT) images is crucial for digital dentistry but remains challenging in cases of interdental adhesions, which cause severe anatomical shape dist…
Multi-Label LearningMulti-Task LearningSemi-Supervised Federated Multi-Label Feature Selection with Fuzzy Information Measures
Multi-label feature selection (FS) reduces the dimensionality of multi-label data by removing irrelevant, noisy, and redundant features, thereby boosting the performance of multi-label learning models. However, existing …
Multi-Label LearningLate-decoupled 3D Hierarchical Semantic Segmentation with Semantic Prototype Discrimination based Bi-branch Supervision
3D hierarchical semantic segmentation (3DHS) is crucial for embodied intelligence applications that demand a multi-grained and multi-hierarchy understanding of 3D scenes. Despite the progress, previous 3DHS methods have …
Semantic SegmentationMulti-Label LearningLarge 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 LearningGenePheno: Interpretable Gene Knockout-Induced Phenotype Abnormality Prediction from Gene Sequences
Exploring how genetic sequences shape phenotypes is a fundamental challenge in biology and a key step toward scalable, hypothesis-driven experimentation. The task is complicated by the large modality gap between sequence…
Multi-Label 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 LearningCuriosity Meets Cooperation: A Game-Theoretic Approach to Long-Tail Multi-Label Learning
Long-tail imbalance is endemic to multi-label learning: a few head labels dominate the gradient signal, while the many rare labels that matter in practice are silently ignored. We tackle this problem by casting the task …
Multi-Label LearningHyperbolic Structured Classification for Robust Single Positive Multi-label Learning
Single Positive Multi-Label Learning (SPMLL) addresses the challenging scenario where each training sample is annotated with only one positive label despite potentially belonging to multiple categories, making it difficu…
Multi-Label LearningAdaptive 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 LearningRethinking 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 LearningLAMP-PRo: Label-aware Attention for Multi-label Prediction of DNA- and RNA-binding Proteins using Protein Language Models
Identifying DNA- (DBPs) and RNA-binding proteins (RBPs) is crucial for the understanding of cell function, molecular interactions as well as regulatory functions. Owing to their high similarity, most of the existing appr…
Protein Language ModelMulti-Label LearningLeast-Ambiguous Multi-Label Classifier
Multi-label learning often requires identifying all relevant labels for training instances, but collecting full label annotations is costly and labor-intensive. In many datasets, only a single positive label is annotated…
Multi-Label LearningThe Demon is in Ambiguity: Revisiting Situation Recognition with Single Positive Multi-Label Learning
Context recognition (SR) is a fundamental task in computer vision that aims to extract structured semantic summaries from images by identifying key events and their associated entities. Specifically, given an input image…
Semantic Role LabelingSituation RecognitionMulti-Label LearningMore 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 LearningOnline 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 LearningExploring Partial Multi-Label Learning via Integrating Semantic Co-occurrence Knowledge
Partial multi-label learning aims to extract knowledge from incompletely annotated data, which includes known correct labels, known incorrect labels, and unknown labels. The core challenge lies in accurately identifying …
Multi-Label LearningNoise-Resistant Label Reconstruction Feature Selection for Partial Multi-Label Learning
The "Curse of dimensionality" is prevalent across various data patterns, which increases the risk of model overfitting and leads to a decline in model classification performance. However, few studies have focused on this…
feature selectionMulti-Label LearningCut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
Multi-Label Online Continual Learning (MOCL) requires models to learn continuously from endless multi-label data streams, facing complex challenges including persistent catastrophic forgetting, potential missing labels, …
Continual LearningMissing LabelsMulti-Label LearningFairPO: Robust Preference Optimization for Fair Multi-Label Learning
We propose FairPO, a novel framework designed to promote fairness in multi-label classification by directly optimizing preference signals with a group robustness perspective. In our framework, the set of labels is partit…
FairnessMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningGPMFS: Global Foundation and Personalized Optimization for Multi-Label Feature Selection
As artificial intelligence methods are increasingly applied to complex task scenarios, high dimensional multi-label learning has emerged as a prominent research focus. At present, the curse of dimensionality remains one …
feature selectionMulti-Label Learning