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Multi-label zero-shot learning

The goal of multi-label classification task is to predict a set of labels in an image. As an extension of zero-shot learning (ZSL), multi-label zero-shot learning (ML-ZSL) is developed to identify multiple seen and unseen labels in an image.

Papers

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Showing 11–20 of 27 papers

TitleStatusHype
Open Vocabulary Multi-Label Classification with Dual-Modal Decoder on Aligned Visual-Textual Features—0
Epsilon: Exploring Comprehensive Visual-Semantic Projection for Multi-Label Zero-Shot Learning—0
Fast Zero-Shot Image Tagging—0
GBE-MLZSL: A Group Bi-Enhancement Framework for Multi-Label Zero-Shot Learning—0
(ML)^2P-Encoder: On Exploration of Channel-Class Correlation for Multi-Label Zero-Shot Learning—0
Multi-Label Zero-Shot Human Action Recognition via Joint Latent Ranking Embedding—0
Multi-Label Zero-Shot Learning via Concept Embedding—0
Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection—0
Query-Based Knowledge Sharing for Open-Vocabulary Multi-Label Classification—0
Towards Unbiased Multi-label Zero-Shot Learning with Pyramid and Semantic Attention—0
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