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A Survey Of zero shot detection: Methods and applications
Cognitive Robotics, 2021 Zero shot learning (ZSL) is aim to identify objects whose label is unavailable during training. This learning paradigm makes classifier has the ability to distinguish unseen class. The traditional ZSL method only focuses on the image recognition problems
Chufeng Tan, Xing Xu, Fumin Shen
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Survey of Zero-Shot Image Classification
Jisuanji kexue yu tansuo, 2021 It is time-consuming and laborious to manually label a large number of samples, and samples from some rare classes are difficult to obtain. Therefore, the zero-shot image classification has become a research hotspot in the computer vision field. Firstly,
LIU Jingyi, SHI Caijuan, TU Dongjing, LIU Shuai
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RS-CLIP: Zero shot remote sensing scene classification via contrastive vision-language supervision
International Journal of Applied Earth Observations and Geoinformation, 2023 Zero-shot remote sensing scene classification aims to solve the scene classification problem on unseen categories and has attracted numerous research attention in the remote sensing field.
Xiang Li +3 more
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Zero-shot Image Classification Method Based on Discriminator Feedback
Journal of Harbin University of Science and Technology, 2023 Zero-shot learning (ZSL) strives to classify unseen categories for which no data is available during training.At present, among generative methods, zero-shot learning based on joint generative model VAEGAN is a research hotspot.On this basis, we propose ...
FAN Yufei, DING Bo, HE Yongjun
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Zero-Shot Image Classification Based on Improved Variational Auto-encoder
Taiyuan Ligong Daxue xuebao, 2021 In the process of zero-shot image classification, problems such as high acquisition cost for samples of known categories and domain drift were addressed.
Zhen CAO, Hongwei XIE
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Scalable Zero-Shot Logo Recognition
IEEE Access, 2023 Brand logo recognition is a task focused on the identification and classification of logos, with various applications such as brand protection and market discovery.
Mikhail Shulgin, Ilya Makarov
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Variational Disentangle Zero-Shot Learning
Mathematics, 2023 Existing zero-shot learning (ZSL) methods typically focus on mapping from the feature space (e.g., visual space) to class-level attributes, often leading to a non-injective projection.
Jie Su +4 more
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Applied Sciences, 2019 Traditional supervised learning is dependent on the label of the training data, so there is a limitation that the class label which is not included in the training data cannot be recognized properly.
Sanghyun Seo, Juntae Kim
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Prognostication of Unseen Objects using Zero-Shot Learning with a Complete Case Analysis [PDF]
Interdisciplinary Description of Complex Systems, 2022 Generally, for a machine learning model to perform well, the data instances on which the model is being trained have to be relevant to the use case.
Srinivasa L. Chakravarthy +1 more
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Dual Generative Network with Discriminative Information for Generalized Zero-Shot Learning
Complexity, 2021 Zero-shot learning is dedicated to solving the classification problem of unseen categories, while generalized zero-shot learning aims to classify the samples selected from both seen classes and unseen classes, in which “seen” and “unseen” classes ...
Tingting Xu, Ye Zhao, Xueliang Liu
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