Integrated generalized zero-shot learning for fine-grained classification

- Shermin, Tasfia, Teng, Shyh, Sohel, Ferdous, Murshed, Manzur, Lu, Guojun

Title
Integrated generalized zero-shot learning for fine-grained classification
Creator
Shermin, Tasfia; Teng, Shyh; Sohel, Ferdous; Murshed, Manzur; Lu, Guojun
Date
2022
Type
Text; Journal article
Identifier
http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/179718
Identifier
vital:15672
Identifier
https://doi.org/10.1016/j.patcog.2021.108246
Identifier
ISBN:0031-3203 (ISSN)
Abstract
Embedding learning (EL) and feature synthesizing (FS) are two of the popular categories of fine-grained GZSL methods. EL or FS using global features cannot discriminate fine details in the absence of local features. On the other hand, EL or FS methods exploiting local features either neglect direct attribute guidance or global information. Consequently, neither method performs well. In this paper, we propose to explore global and direct attribute-supervised local visual features for both EL and FS categories in an integrated manner for fine-grained GZSL. The proposed integrated network has an EL sub-network and a FS sub-network. Consequently, the proposed integrated network can be tested in two ways. We propose a novel two-step dense attention mechanism to discover attribute-guided local visual features. We introduce new mutual learning between the sub-networks to exploit mutually beneficial information for optimization. Moreover, we propose to compute source-target class similarity based on mutual information and transfer-learn the target classes to reduce bias towards the source domain during testing. We demonstrate that our proposed method outperforms contemporary methods on benchmark datasets. © 2021 Elsevier Ltd
Publisher
Elsevier Ltd
Relation
Pattern Recognition Vol. 122, no. (2022), p.
Rights
All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
Rights
Copyright © 2021 Elsevier Ltd.
Rights
Open Access
Subject
0801 Artificial Intelligence and Image Processing; 0806 Information Systems; 0906 Electrical and Electronic Engineering; Dense attention mechanism; Fine-grained classification; Generalized zero-shot learning
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