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Zero-shot classification with unseen prototype learning

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0 Published in 2021 at "Neural Computing and Applications"

DOI: 10.1007/s00521-021-05746-9

Abstract: Zero-shot learning (ZSL) aims at recognizing instances from unseen classes via training a classification model with only seen data. Most existing approaches easily suffer from the classification bias from unseen to seen categories since the… read more here.

Keywords: unseen prototype; zsl; prototype learning; zero shot ... See more keywords

NucNormZSL: nuclear norm-based domain adaptation in zero-shot learning

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0 Published in 2021 at "Neural Computing and Applications"

DOI: 10.1007/s00521-021-06461-1

Abstract: The ability of human beings to recognize novel concepts has attracted significant attention in the research community. Zero-shot learning, also known as zero-data learning, seeks to build models that can recognize novel class instances even… read more here.

Keywords: nuclear norm; shot learning; class; zero shot ... See more keywords

JSE: Joint Semantic Encoder for zero-shot gesture learning

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1 Published in 2021 at "Pattern Analysis and Applications"

DOI: 10.1007/s10044-021-00992-y

Abstract: Zero-shot learning (ZSL) is a transfer learning paradigm that aims to recognize unseen categories just by having a high-level description of them. While deep learning has greatly pushed the limits of ZSL for object classification,… read more here.

Keywords: semantic encoder; encoder; joint semantic; zero shot ... See more keywords

Zero-shot Fine-grained Classification by Deep Feature Learning with Semantics

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0 Published in 2019 at "International Journal of Automation and Computing"

DOI: 10.1007/s11633-019-1177-8

Abstract: Fine-grained image classification, which aims to distinguish images with subtle distinctions, is a challenging task for two main reasons: lack of sufficient training data for every class and difficulty in learning discriminative features for representation.… read more here.

Keywords: classification; fine grained; zero shot; shot fine ... See more keywords

Discriminant Zero-Shot Learning with Center Loss

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0 Published in 2019 at "Cognitive Computation"

DOI: 10.1007/s12559-019-09629-z

Abstract: Current work on zero-shot learning (ZSL) generally does not focus on the discriminative ability of the models, which is important for differentiating between classes since our brain focuses on the discriminating part of the object… read more here.

Keywords: zero shot; discriminant zero; shot learning; center loss ... See more keywords

Zero-shot policy generation in lifelong reinforcement learning

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0 Published in 2021 at "Neurocomputing"

DOI: 10.1016/j.neucom.2021.02.058

Abstract: Abstract Lifelong reinforcement learning (LRL) is an important approach to achieve continual lifelong learning of multiple reinforcement learning tasks. The two major methods used in LRL are task decomposition and policy knowledge extraction. Policy knowledge… read more here.

Keywords: reinforcement learning; zero shot; policy; knowledge ... See more keywords

Sequence feature generation with temporal unrolling network for zero-shot action recognition

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0 Published in 2021 at "Neurocomputing"

DOI: 10.1016/j.neucom.2021.03.070

Abstract: Abstract Zero-Shot Action Recognition (ZSAR) aims to recognize unseen action classes not included in the training dataset. Existing generative methods for ZSAR synthesize a feature of unseen action from a class embedding to overcome the… read more here.

Keywords: zero shot; feature; sequence; action ... See more keywords

Context-sensitive zero-shot semantic segmentation model based on meta-learning

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0 Published in 2021 at "Neurocomputing"

DOI: 10.1016/j.neucom.2021.08.120

Abstract: Abstract The zero-shot semantic segmentation requires models with a strong image understanding ability. The majority of current solutions are based on direct mapping or generation. These schemes are effective in dealing with the zero-shot recognition,… read more here.

Keywords: shot semantic; meta learning; shot; zero shot ... See more keywords

DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms

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1 Published in 2022 at "Bioinformatics"

DOI: 10.1101/2022.01.14.476325

Abstract: Motivation Protein functions are often described using the Gene Ontology (GO) which is an ontology consisting of over 50,000 classes and a large set of formal axioms. Predicting the functions of proteins is one of… read more here.

Keywords: ontology; prediction; zero shot; protein function ... See more keywords

Transfer Feature Generating Networks With Semantic Classes Structure for Zero-Shot Learning

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0 Published in 2019 at "IEEE Access"

DOI: 10.1109/access.2019.2958052

Abstract: Feature generating networks face a very important issue, which is the fitting difference (inconsistency) of the distribution between the generated feature and the real data. This inconsistency further influences the performance of the network model… read more here.

Keywords: semantic classes; shot learning; generating networks; zero shot ... See more keywords

Discriminative Embedding Autoencoder With a Regressor Feedback for Zero-Shot Learning

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0 Published in 2020 at "IEEE Access"

DOI: 10.1109/access.2020.2964613

Abstract: Zero-shot learning (ZSL) aims to recognize the novel object categories using the semantic representation of categories, and the key idea is to explore the knowledge of how the novel class is semantically related to the… read more here.

Keywords: shot learning; regressor feedback; discriminative embedding; zero shot ... See more keywords