Zobrazeno 1 - 10
of 390
pro vyhledávání: '"few-shot classification"'
Publikováno v:
IEEE Access, Vol 12, Pp 174133-174143 (2024)
Deep learning models face significant challenges in image classification due to the limited availability of training samples. To address this issue, few-shot learning, which enables model training with a small number of samples, has emerged. When app
Externí odkaz:
https://doaj.org/article/4e06e72a12d042c9bb434950759e50ec
Publikováno v:
EURASIP Journal on Image and Video Processing, Vol 2024, Iss 1, Pp 1-15 (2024)
Abstract Multimodal few-shot learning aims to exploit complementary information inherent in multiple modalities for vision tasks in low data scenarios. Most of the current research focuses on a suitable embedding space for the various modalities. Whi
Externí odkaz:
https://doaj.org/article/27bddd7b0cbb4f01b319821a797e89c1
Publikováno v:
Remote Sensing, Vol 16, Iss 13, p 2453 (2024)
Leveraging the open-world understanding capacity of large-scale visual-language pre-trained models has become a hot spot in point cloud classification. Recent approaches rely on transferable visual-language pre-trained models, classifying point cloud
Externí odkaz:
https://doaj.org/article/c4d21cf7425d4cbdbda53bd4d000a18d
Publikováno v:
Remote Sensing, Vol 16, Iss 12, p 2238 (2024)
Automatic recognition of species is important for the conservation and management of biodiversity. However, since closely related species are visually similar, it is difficult to distinguish them by images alone. In addition, traditional species-reco
Externí odkaz:
https://doaj.org/article/dfb69869fc0944408228f0b9ee8e8ef7
Publikováno v:
Frontiers in Neurorobotics, Vol 17 (2023)
The objective of few-shot fine-grained learning is to identify subclasses within a primary class using a limited number of labeled samples. However, many current methodologies rely on the metric of singular feature, which is either global or local. I
Externí odkaz:
https://doaj.org/article/0a1c2175127946f5aa7406cfb9910151
Publikováno v:
IET Computer Vision, Vol 17, Iss 1, Pp 62-75 (2023)
Abstract Few‐shot classification (FSC) aims at classifying query samples into correct classes given only a few labelled samples. Prototypical Classifier (PC) can be chosen to be an ideal classifier for settling this problem, as it has good properti
Externí odkaz:
https://doaj.org/article/50ca09f780144043ad36cc78b5113c2b
Publikováno v:
IEEE Access, Vol 11, Pp 82665-82673 (2023)
Cross-domain few-shot classification (CD-FSC) aims to develop few-shot classification models trained on seen domains but tested on unseen domains. However, the cross-domain setup poses a challenge in the form of domain shift between the training and
Externí odkaz:
https://doaj.org/article/ac25d389a03943fdad5ea96385f59365
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 123, Iss , Pp 103447- (2023)
Few-shot learning is an important and challenging research topic for remote sensing image scene classification. Many existing approaches address this challenge by using meta-learning and metric-learning techniques, which aim to develop feature extrac
Externí odkaz:
https://doaj.org/article/52271524ca4a47d9835274f9e87321ee
Publikováno v:
Applied Sciences, Vol 14, Iss 6, p 2361 (2024)
Litopenaeus vannamei is a common species in aquaculture and has a high economic value. However, Litopenaeus vannamei are often invaded by pathogenic bacteria and die during the breeding process, so it is of great significance to study the identificat
Externí odkaz:
https://doaj.org/article/2049b563fa1946be96721e9bf050568a
Publikováno v:
Sensors, Vol 24, Iss 6, p 1815 (2024)
In recent computer vision research, the pursuit of improved classification performance often leads to the adoption of complex, large-scale models. However, the actual deployment of such extensive models poses significant challenges in environments co
Externí odkaz:
https://doaj.org/article/8156535de34746cda9a79d1207b05158