Discriminative comparison classifier for generalized zero-shot learning

Hou, Mingzhen; Xia, Wei; Zhang, Xiangdong; Gao, Quanxue

doi:10.1016/j.neucom.2020.07.030

Cited by 12 publications

(4 citation statements)

References 10 publications

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“…Sung et al [15] recently propose relation network (RN) between adaptive learning visual features and semantic attributes. Several works [17]- [19] propose methods to mitigate the problem of domain bias based on relational networks.…”

Section: Embedding Methodsmentioning

confidence: 99%

A Zero-Shot Learning Relationship Classification Method with Semantic Pivot Regularization

Tang

et al. 2023

Preprint

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Zero-shot learning (ZSL) can identify new classes by transferring semantic knowledge from seen classes to unseen classes according to the relationships between the visual and semantic features of learning. However, due to the difference of manifold distribution between visual feature space and semantic feature space, some algorithms rely too much on cross-modal mapping between semantic feature space and visual feature space, and this process lacks effective constraints. When mapping from high-dimensional visual feature space to semantic space, highly overlapping semantic attributes are generated, which leads to the problem of pivot point. In addition, there is a serious problem of domain migration in generalized zero-shot learning. In order to solve these problems, we propose the Zero-shot learning relationship method (SPR-ZSL) with semantic pivot regularization. In SPR-ZSL, semantic attributes of low-dimensional features are first mapped to visual space to alleviate the hubness problem. Then semantic pivot regularization loss is designed to guide the embedding network to learn semantic attributes to make the original semantic attributes more discriminative. Finally, the relation network is used to learn the similarity between semantic attributes and image features adaptively to complete the image classification task. The effectiveness of the proposed method is validated on five datasets for both traditional Zero-shot learning and generalized Zero-shot learning tasks. The proposed method is compared with other state-of-the-art methods, and the effectiveness is demonstrated through comprehensive analysis of the experimental results.

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Section: Embedding Methodsmentioning

confidence: 99%

A Zero-Shot Learning Relationship Classification Method with Semantic Pivot Regularization

Tang

et al. 2023

Preprint

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“…to improve the visual-semantic mapping by adopting different loss functions or different mapping functions [21,22,23,24,25,26,27,28,29]. For instance, ALE [22] employed a bilinear compatibility function to embed the visual features and semantic features.…”

Section: Zero-shot Learningmentioning

confidence: 99%

Semantic-diversity transfer network for generalized zero-shot learning via inner disagreement based OOD detector

Liu¹,

Dong²,

Hu³

2022

Preprint

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Zero-shot learning (ZSL) aims to recognize objects from unseen classes, where the kernel problem is to transfer knowledge from seen classes to unseen classes by establishing appropriate mappings between visual and semantic features.The knowledge transfer in many existing works is limited mainly due to the facts that (i) the widely used visual features are global ones but not totally consistent with semantic attributes; (ii) only one mapping is learned in existing works, which is not able to effectively model diverse visual-semantic relations;(iii) the bias problem in the generalized ZSL (GZSL) could not be effectively handled. In this paper, we propose two techniques to alleviate these limitations.Firstly, we propose a Semantic-diversity transfer Network (SetNet) addressing the first two limitations, where 1) a multiple-attention architecture and a diversity regularizer are proposed to learn multiple local visual features that are more consistent with semantic attributes and 2) a projector ensemble that geometrically takes diverse local features as inputs is proposed to model visual-semantic relations from diverse local perspectives. Secondly, we propose an inner dis-

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“…CPL [ 8 ] learned visual prototype representations for unseen classes to solve the problem. To obtain discriminative prototype, DVBE [ 20 ] used second-order graphical statistics, DCC [ 21 ] learned the relationship between embedded features and visual features, and HSVA [ 22 ] used hierarchical two-step adaptive alignment of visual and semantic feature manifolds. However, the prototype representation is constrained and does not correspond to actual features [ 10 ] due to domain shift.…”

Section: Related Workmentioning

confidence: 99%

A Distinguishable Pseudo-Feature Synthesis Method for Generalized Zero-Shot Learning

Jia

Liu

et al. 2022

Computational Intelligence and Neuroscience

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Generalized zero-shot learning (GZSL) aims to classify seen classes and unseen classes that are disjoint simultaneously. Hybrid approaches based on pseudo-feature synthesis are currently the most popular among GZSL methods. However, they suffer from problems of negative transfer and low-quality class discriminability, causing poor classification accuracy. To address them, we propose a novel GZSL method of distinguishable pseudo-feature synthesis (DPFS). The DPFS model can provide high-quality distinguishable characteristics for both seen and unseen classes. Firstly, the model is pretrained by a distance prediction loss to avoid overfitting. Then, the model only selects attributes of similar seen classes and makes sparse representations based on attributes for unseen classes, thereby overcoming negative transfer. After the model synthesizes pseudo-features for unseen classes, it disposes of the pseudo-feature outliers to improve the class discriminability. The pseudo-features are fed into a classifier of the model together with features of seen classes for GZSL classification. Experimental results on four benchmark datasets verify that the proposed DPFS has GZSL classification performance better than that in existing methods.

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Discriminative comparison classifier for generalized zero-shot learning

Cited by 12 publications

References 10 publications

A Zero-Shot Learning Relationship Classification Method with Semantic Pivot Regularization

A Zero-Shot Learning Relationship Classification Method with Semantic Pivot Regularization

Semantic-diversity transfer network for generalized zero-shot learning via inner disagreement based OOD detector

A Distinguishable Pseudo-Feature Synthesis Method for Generalized Zero-Shot Learning

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