SAM‐dPCR: Accurate and Generalist Nuclei Acid Quantification Leveraging the Zero‐Shot Segment Anything Model
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DOI: 10.1002/advs.202406797
Abstract: Digital PCR (dPCR) has transformed nucleic acid diagnostics by enabling the absolute quantification of rare mutations and target sequences. However, traditional dPCR detection methods, such as those involving flow cytometry and fluorescence imaging, may face… read more here.
Keywords: segment anything; quantification; shot segment; zero shot ... See more keywords
Zero-shot classification with unseen prototype learning
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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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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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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 plant disease classification with semantic attributes
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DOI: 10.1007/s10462-024-10950-9
Abstract: In the rapidly evolving field of plant disease detection, the number and complexity of crop diseases are increasing, made worse by factors like climate change. Addressing these challenges requires robust and efficient methodologies capable of… read more here.
Keywords: disease; plant; zero shot; plant disease ... See more keywords
Zero-shot prompt-based video encoder for surgical gesture recognition
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DOI: 10.1007/s11548-024-03257-1
Abstract: In order to produce a surgical gesture recognition system that can support a wide variety of procedures, either a very large annotated dataset must be acquired, or fitted models must generalize to new labels (so-called… read more here.
Keywords: video encoder; gesture; zero shot; gesture recognition ... See more keywords
Zero-shot Fine-grained Classification by Deep Feature Learning with Semantics
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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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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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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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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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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