AVGZSLNet: Audio-Visual Generalized Zero-Shot Learning by Reconstructing Label Features from Multi-Modal Embeddings

Mazumder, Pratik; Singh, Pravendra; Parida, Kranti Kumar; Namboodiri, Vinay P.

doi:10.48550/arxiv.2005.13402

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2020

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“…As an example, a new multi-label ZSL (MZSL) framework using JLRE (Joint Latent Ranking Embedding) has been proposed in [158]. The relatedness score of various action labels is measured for the test video clips in the semantic embedding and joint latent visual spaces.In addition, a multimodal framework using audio, video, and text has been introduced in [160], [161].…”

Section: Applicationsmentioning

confidence: 99%

A Review of Generalized Zero-Shot Learning Methods

Pourpanah¹,

Abdar²,

Luo³

et al. 2020

Preprint

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Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning. To address this challenging task, GZSL leverages semantic information of both seen (source) and unseen (target) classes to bridge the gap between both seen and unseen classes. Since its introduction, many GZSL models have been formulated. In this review paper, we present a comprehensive review of GZSL. Firstly, we provide an overview of GZSL including the problems and challenging issues. Then, we introduce a hierarchical categorization of the GZSL methods and discuss the representative methods of each category. In addition, we discuss several research directions for future studies.

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Section: Applicationsmentioning

confidence: 99%