Multi-Label Learning
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Benchmarks
COCO 2014
Most implemented
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
A Survey on Extreme Multi-label Learning
MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
Papers
Adapting Vision-Language Models from Iconic to Inclusive for Multi-Label Recognition Without Labels
Understanding multi-label images remains a challenging task in computer vision. With the rapid progress of vision-language multimodal learning, vision-language models (VLMs) enable zero-shot recognition without labeled d…
Multi-Label LearningPrincipled Algorithms for Optimizing Generalized Metrics in Multi-Label Learning
Many real-world classification tasks require predicting multiple labels per instance, necessitating the optimization of complex evaluation metrics such as the $F$-measure and Jaccard index. While the Empirical Utility Ma…
Multi-Label LearningCoherent Hierarchical Multi-Label Learning to Defer for Medical Imaging
Learning to Defer (L2D) enables a model to predict autonomously or defer to an expert, but prior work largely assumes flat label spaces. We study the first L2D setting with hierarchical multi-label decisions, motivated b…
Multi-Label LearningHow Label Imbalance Shapes Geometry: A General Spectral Analysis of Multi-Label Neural Collapse
This work investigates the phenomenon of Neural Collapse (NC) in multi-label classification, extending its conceptual framework from multi-class learning to general correlated and imbalanced multi-label settings. Althoug…
Multi-Label ClassificationMulti-Label LearningFedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning
Federated Multi-Label Learning is a distributed paradigm where multiple clients possess heterogeneous multi-label data and perform collaborative learning under privacy constraints without sharing raw data. However, model…
Multi-Label LearningFeature-Label Modal Alignment for Robust Partial Multi-Label Learning
In partial multi-label learning (PML), each instance is associated with a set of candidate labels containing both ground-truth and noisy labels. The presence of noisy labels disrupts the correspondence between features a…
Multi-Label Learning