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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
Zero-shot Learning for Audio-based Music Classification and TaggingCode0
CLIP-Decoder : ZeroShot Multilabel Classification using Multimodal CLIP Aligned RepresentationCode0
Label-Embedding for Image ClassificationCode0
Multi-Label Zero-Shot Learning with Structured Knowledge GraphsCode0
Zero-Shot Learning by Convex Combination of Semantic EmbeddingsCode0
Multi-Label Zero-Shot Learning via Concept Embedding—0
Open Vocabulary Multi-Label Classification with Dual-Modal Decoder on Aligned Visual-Textual Features—0
Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection—0
GBE-MLZSL: A Group Bi-Enhancement Framework for Multi-Label Zero-Shot Learning—0
Fast Zero-Shot Image Tagging—0
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