An Exploration of Cross-Modal Retrieval for Unseen Concepts

Zhong, Fangming; Chen, Zhikui; Min, Geyong

doi:10.1007/978-3-030-18579-4_2

Cited by 4 publications

(1 citation statement)

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“…With the explosive growth of new concepts on the Web, it is infeasible to collect sufficient labeled data to train a recognition model [1,2]. To this end, zero-shot learning (ZSL), which is motivated by humans' cognition of new concepts has been studied considerably [3].…”

Section: Introductionmentioning

confidence: 99%

Multiple-Input Multiple-Output Fusion Network for Generalized Zero-Shot Learning

Zhong

Wang

Chen

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Generalized zero-shot learning (GZSL) has attracted considerable attention recently, which trains models with data from seen classes and tests on data from both seen and unseen classes. Most of the existing methods attempt to find a mapping from visual space to semantic space, such mapping can easily result in the domain shift problem. To address this issue, we propose a Multiple-Input Multiple-Output Fusion Network to GZSL. It can generate similar common semantic representation to paired inputs even with only the class semantic embeddings. This makes it possible to synthesize pseudo samples from attributes of unseen classes. Extensive experiments carried out on three benchmark datasets show the effectiveness of the proposed model.

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Section: Introductionmentioning

confidence: 99%