Adaptive Margin-based Contrastive Network for Generalized Zero-Shot Learning

被引：2

作者：

Lee, Jeong-Cheol ^{[1
]}

Shibu, Athul ^{[1
]}

Lee, Dong-Gyu ^{[1
]}

机构：

[1] Kyungpook Natl Univ, Dept Artificial Intelligence, Daegu, South Korea

来源：

2023 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS, ICCE | 2023年

基金：

新加坡国家研究基金会;

关键词：

Adaptive margin; contrastive network; deep learning; generalized zero-shot learning; zero-shot learning;

D O I：

10.1109/ICCE56470.2023.10043553

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Generalized zero-shot learning is a challenging problem that aims to recognize images from seen and unseen classes. Recent methods are costly and time-consuming or have a bias problem. To tackle this problem, we proposed an adaptive margin-based contrastive network that aims to distinguish similar classes in generalized zero-shot learning. The proposed method employs the architecture of transferable contrastive network to classify unseen classes and adaptive margin to transfer discriminative knowledge. Experiments on the AwA2 dataset demonstrate competitive results against state-of-the-art benchmarks.

引用

页数：4

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