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Meta-learning for few-shot open task recognition [PDF]
Scientific ReportsCurrent few-shot learning research often assumes predefined task configurations and evaluates under fixed N-way K-shot settings. In realistic deployments, the target configuration is unknown at training time, and both way and shot can differ from the ...
Xiaoming Han +5 more
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Federated Self-Supervised Few-Shot Face Recognition [PDF]
Journal of ImagingThis paper presents a systematic framework that combines federated learning, self-supervised learning, and few-shot learning paradigms for privacy-preserving face recognition.
Nursultan Makhanov +3 more
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Few-shot learning-based human activity recognition [PDF]
Expert Systems With Applications, 2019 Few-shot learning is a technique to learn a model with a very small amount of labeled training data by transferring knowledge from relevant tasks. In this paper, we propose a few-shot learning method for wearable sensor based human activity recognition, a technique that seeks high-level human activity knowledge from low-level sensor inputs.
Siwei Feng, Marco F Duarte
exaly +3 more sources
Few-shot prototype adaptation for generalizable electromyography gesture recognition [PDF]
Scientific ReportsWe present EMG-Adapt, a novel few-shot prototype adaptation framework designed to enhance the robustness and data efficiency of electromyography (EMG)-based gesture recognition.
Hunmin Lee +4 more
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A Review on the Few-Shot SAR Target Recognition
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingSynthetic aperture radar (SAR) has the advantage of providing imaging capabilities throughout the day and under all-weather conditions, which makes it particularly important for Earth observation applications.
Junjun Yin +3 more
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Dataset Bias in Few-Shot Image Recognition [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 The goal of few-shot image recognition (FSIR) is to identify novel categories with a small number of annotated samples by exploiting transferable knowledge from training data (base categories). Most current studies assume that the transferable knowledge can be well used to identify novel categories. However, such transferable capability may be impacted
Shuqiang Jiang +5 more
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Few‐shot action recognition using task‐adaptive parameters
Electronics Letters, 2021 Few‐shot action recognition aims to recognise unseen actions given a few examples. Thus, this letter proposes a model named meta relation network (Meta RN) to address such problem. This model contains two parts: a MetaNet and a relation network. Relation
Pengcheng Zong +4 more
doaj +1 more source
Elastic temporal alignment for few‐shot action recognition
IET Computer Vision, 2023 Few‐shot action recognition aims to learn a classification model with good generalisation ability when trained with only a few labelled videos. However, it is difficult to learn discriminative feature representations for videos in such a setting.
Fei Pan +4 more
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Few-Shot Image Recognition with Manifolds [PDF]
, 2020 International Symposium on Visual Computing (ISVC ...
Debasmit Das +2 more
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Knowledge Prompting for Few-shot Action Recognition
CoRR, 2022 Few-shot action recognition in videos is challenging for its lack of supervision and difficulty in generalizing to unseen actions. To address this task, we propose a simple yet effective method, called knowledge prompting, which leverages commonsense knowledge of actions from external resources to prompt a powerful pre-trained vision-language model for
Yuheng Shi, Xinxiao Wu, Hanxi Lin
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