A Summary on Few-Shot Object Detection via Transfer Learning
Publish place: 19th National Conference of Computer Science and Engineering and Information Technology
Publish Year: 1402
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
CECCONF19_013
تاریخ نمایه سازی: 28 خرداد 1402
Abstract:
Convolutional neural networks usually require a lot of annotated data for object detection. To avoid the need to acquire and annotate these huge amounts of data, few-shot object detection aims to learn from few object instances of new categories in the target domain. This paper provides a summary on some of the most recent state-of-the-art few shot object detection methods based on transfer learning. Then compare their results on two most common datasets for the task of object detection.
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