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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2023

27.08.2022

Learning Transferable Feature Representation with Swin Transformer for Object Recognition

verfasst von: Jian-Xin Ren, Yu-Jie Xiong, Xi-Jiong Xie, Yu-Fan Dai

Erschienen in: Neural Processing Letters | Ausgabe 3/2023

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Abstract

Recent, substantial advancements in deep learning technologies have driven the flourishing of computer vision. However, the heavy dependence on the scale of training data limits deep learning applications because it is generally hard to obtain such a large number of data in many practical scenarios. And, deep learning seems to offer no significant advantage compared with traditional machine methods in a lack of sufficient training data. The proposed approach in this paper overcomes the problem of insufficient training data by taking Swin Transformer as the backbone for feature extraction and performing the fine-tuning strategies on the target dataset for learning transferable feature representation. Our experimental results demonstrate that the proposed method has a good performance for object recognition on small-scale datasets.
Vorheriger Artikel Intelligent Identification of Jute Pests Based on Transfer Learning and Deep Convolutional Neural Networks
Nächster Artikel Efficient Deep Feature Based Semantic Image Retrieval

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Metadaten
Titel
Learning Transferable Feature Representation with Swin Transformer for Object Recognition
verfasst von
Jian-Xin Ren
Yu-Jie Xiong
Xi-Jiong Xie
Yu-Fan Dai
Publikationsdatum
27.08.2022
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2023
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-11004-3

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