부산대학교 도서관

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.