Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142785
Title: Zero-shot learning via category-specific visual-semantic mapping and label refinement
Authors: Niu, Li
Cai, Jianfei
Veeraraghavan, Ashok
Zhang, Liqing
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Niu, L., Cai, J., Veeraraghavan, A., & Zhang, L. (2019). Zero-shot learning via category-specific visual-semantic mapping and label refinement. IEEE Transactions on Image Processing, 28(2), 965-979. doi:10.1109/tip.2018.2872916
Journal: IEEE Transactions on Image Processing
Abstract: Zero-shot learning (ZSL) aims to classify a test instance from an unseen category based on the training instances from seen categories in which the gap between seen categories and unseen categories is generally bridged via visual-semantic mapping between the low-level visual feature space and the intermediate semantic space. However, the visual-semantic mapping (i.e., projection) learnt based on seen categories may not generalize well to unseen categories, which is known as the projection domain shift in ZSL. To address this projection domain shift issue, we propose a method named adaptive embedding ZSL (AEZSL) to learn an adaptive visual-semantic mapping for each unseen category, followed by progressive label refinement. Moreover, to avoid learning visual-semantic mapping for each unseen category in the large-scale classification task, we additionally propose a deep adaptive embedding model named deep AEZSL sharing the similar idea (i.e., visual-semantic mapping should be category specific and related to the semantic space) with AEZSL, which only needs to be trained once, but can be applied to arbitrary number of unseen categories. Extensive experiments demonstrate that our proposed methods achieve the state-of-the-art results for image classification on three small-scale benchmark datasets and one large-scale benchmark dataset.
URI: https://hdl.handle.net/10356/142785
ISSN: 1057-7149
DOI: 10.1109/TIP.2018.2872916
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TIP.2018.2872916.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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