GenDet: Meta Learning to Generate Detectors From Few Shots

Object detection has made enormous progress and has been widely used in many applications. However, it performs poorly when only limited training data is available for novel classes that the model has never seen before. Most existing approaches solve few-shot detection tasks implicitly without direc...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2022-08, Vol.33 (8), p.3448-3460
Hauptverfasser:	Liu, Liyang, Wang, Bochao, Kuang, Zhanghui, Xue, Jing-Hao, Chen, Yimin, Yang, Wenming, Liao, Qingmin, Zhang, Wayne
Format:	Artikel
Sprache:	eng
Schlagworte:	Data models Detectors Feature extraction Few-shot learning Generators Learning meta learning Noise measurement object detection Object recognition Sensors Task analysis Training weight generation
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