LGSim: local task-invariant and global task-specific similarity for few-shot classification
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DOI: 10.1007/s00521-020-04750-9
Abstract: Few-shot learning is one of the most challenging problems in computer vision due to the difficulty of sample collection in many real-world applications. It aims at classifying a sample when the number of training samples… read more here.
Keywords: sample; task; similarity; task specific ... See more keywords
Calibration of Few-Shot Classification Tasks: Mitigating Misconfidence from Distribution Mismatch
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DOI: 10.1109/access.2022.3176090
Abstract: As many meta-learning algorithms improve performance in solving few-shot classification problems for practical applications, the accurate prediction of uncertainty is considered essential. In meta-training, the algorithm treats all generated tasks equally and updates the model… read more here.
Keywords: classification; shot classification; distribution mismatch; model ... See more keywords
A Multiscale Spatial–Spectral Prototypical Network for Hyperspectral Image Few-Shot Classification
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2022.3185743
Abstract: Due to the complex environment of hyperspectral image (HSI) gathering area, it is difficult to obtain a large number of labeled samples for HSI. Therefore, how to effectively achieve the HSI few-shot classification is a… read more here.
Keywords: shot classification; spatial spectral; hsi; multiscale spatial ... See more keywords
FS-BAN: Born-Again Networks for Domain Generalization Few-Shot Classification
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2023.3266172
Abstract: Conventional Few-shot classification (FSC) aims to recognize samples from novel classes given limited labeled data. Recently, domain generalization FSC (DG-FSC) has been proposed with the goal to recognize novel class samples from unseen domains. DG-FSC… read more here.
Keywords: shot classification; fsc; ban; domain generalization ... See more keywords
Towards Visual Explainable Active Learning for Zero-Shot Classification
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DOI: 10.1109/tvcg.2021.3114793
Abstract: Zero-shot classification is a promising paradigm to solve an applicable problem when the training classes and test classes are disjoint. Achieving this usually needs experts to externalize their domain knowledge by manually specifying a class-attribute… read more here.
Keywords: zero shot; explainable active; active learning; shot classification ... See more keywords