چکیده مقاله
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
کلیدواژهها
نویسندگان
AbdulAli Ahmadi
مقاله A Summary on Few-Shot Object Detection via Transfer Learning
شیوه ارجاع
Ahmadi, AbdulAli,1402,A Summary on Few-Shot Object Detection via Transfer Learning,19th National Conference of Computer Science and Engineering and Information Technology,Babol
ارائهشده در
مجموعه مقالات نوزدهمین کنفرانس ملی علوم و مهندسی کامپیوتر و فناوری اطلاعات28 اردیبهشت 1402 · بابل