Learning Transferable Feature Representation with Swin Transformer for Object Recognition
- Resource Type
- Original Paper
- Authors
- Ren, Jian-Xin; Xiong, Yu-Jie; Xie, Xi-Jiong; Dai, Yu-Fan
- Source
- Neural Processing Letters. :1-13
- Subject
- Transfer learning
Swin transformer
Object recognition
- Language
- English
- ISSN
- 1370-4621
1573-773X
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.