2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2021
DOI: 10.1109/cvprw53098.2021.00292
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DAMSL: Domain Agnostic Meta Score-based Learning

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Cited by 4 publications
(4 citation statements)
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“…Cai et al. (2021) propose a powerful few‐shot cross‐domain method using the Graph Neural Network as a key metric that can work in several specialized fields such as plant disease identification. In short, this type of meta‐learning method is more suitable for few‐shot tasks and can effectively solve the limitation of datasets in plant disease recognition.…”
Section: Methods Of Meta‐learning In Plant Disease Recognitionmentioning
confidence: 99%
See 1 more Smart Citation
“…Cai et al. (2021) propose a powerful few‐shot cross‐domain method using the Graph Neural Network as a key metric that can work in several specialized fields such as plant disease identification. In short, this type of meta‐learning method is more suitable for few‐shot tasks and can effectively solve the limitation of datasets in plant disease recognition.…”
Section: Methods Of Meta‐learning In Plant Disease Recognitionmentioning
confidence: 99%
“…Similarly, Cai et al. (2021) exploit Graph Neural Networks (Garcia & Bruna, 2017) to mine graph knowledge relationships for graphical features of agricultural diseases, achieving favorable migration capability of the model when the study domain changes for few‐shot learning. It is worth noting that, because the PlantVillage dataset also contains disease images of several fruits, the above method also has some ability to identify fruit diseases.…”
Section: Applications Of Meta‐learning In Plant Disease Recognitionmentioning
confidence: 99%
“…Particularly, PMF contributes a simple pipeline and builds a SOTA for FSL. As an extended task from FSL, CD-FSL [1,6,10,11,17,18,25,32,35,42,49,53,57] mainly solves the FSL across different domains. Typical meta-learning based CD-FSL methods include FWT [53], LRP [49], ATA [57], AFA [22], and wave-SAN [11].…”
Section: Related Workmentioning
confidence: 99%
“…Recent study [9] finds that most of the existing FSL methods [12, 15, 17, 28, 37, 37, 39, 41-43, 46, 52-54] that assume the source and target datasets belong to the same distribution fail to generalize to novel datasets with a domain gap. Thus, CD-FSL which aims at addressing FSL across different domains has risen increasing attentions [4,13,14,18,24,33,40,44,48]. In this paper, these CD-FSL methods are categorized according to which kind of data are being used for training: 1) CD-FSL with only source data [14,40,44,48]; 2) CD-FSL with unlabeled target data [24,33]; 3) CD-FSL with labeled target data [13].…”
Section: Related Workmentioning
confidence: 99%