Zero-shot Learning (ZSL) aims to learn a classifier to recognize unseen categories without training samples. Most. ZSL works based on embedding models ...
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Zero-shot Learning (ZSL) aims to learn a classifier to recognize unseen categories without training samples. Most ZSL works based on embedding models handle ...
Extensive experiments on five benchmarks and large-scale Imagenet dataset show that our method can improve the performance, surpassing previous embedding ...
Zero-shot Learning (ZSL) aims to learn a classifier to recognize unseen categories without training samples. Most. ZSL works based on embedding models ...
This work introduces a diversity-based regularizer with the cosine metric which underpins the assumption about the uniform distribution and further improves ...
Model-Agnostic Metric for Zero-Shot Learning | Request PDF
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In this study, we propose a model named SS-CADA-VAE, which includes two single-layer variational autoencoder networks for visual and textual modalities. ...
Supplementary Material of Model-Agnostic Metric for Zero-Shot Learning. Jiayi Shen1, Haochen Wang1, Anran Zhang1, Qiang Qiu2, Xiantong Zhen3, Xianbin Cao1,4 ...
To this end, we propose INTEND, a zero-shot INTENt Detection methodology that leverages contrastive transfer learning and employs a zero-shot learning paradigm ...
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May 3, 2024 · This paper introduces a novel framework for zero-shot learning (ZSL), i.e., to recognize new categories that are unseen during training, ...
Aug 24, 2023 · Algorithms for Few-Shot image classification – Model-Agnostic Meta-Learning, Matching, Prototypical and Relation Networks; Few-Shot Object ...