Generalized Few-Shot Object Detection without Forgetting

• URL: http://arxiv.org/abs/2105.09491v1
• Date: Thu, 20 May 2021 03:25:29 GMT
• Title: Generalized Few-Shot Object Detection without Forgetting
• Authors: Zhibo Fan, Yuchen Ma, Zeming Li, Jian Sun
• Abstract summary: We propose a simple yet effective few-shot detector, Retentive R-CNN. It consists of Bias-Balanced RPN to debias the pretrained RPN and Re-detector to find few-shot class objects without forgetting previous knowledge. Our approach has demonstrated that the long desired never-forgetting learner is available in object detection.
• Score: 17.719042946397487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently few-shot object detection is widely adopted to deal with data-limited situations. While most previous works merely focus on the performance on few-shot categories, we claim that detecting all classes is crucial as test samples may contain any instances in realistic applications, which requires the few-shot detector to learn new concepts without forgetting. Through analysis on transfer learning based methods, some neglected but beneficial properties are utilized to design a simple yet effective few-shot detector, Retentive R-CNN. It consists of Bias-Balanced RPN to debias the pretrained RPN and Re-detector to find few-shot class objects without forgetting previous knowledge. Extensive experiments on few-shot detection benchmarks show that Retentive R-CNN significantly outperforms state-of-the-art methods on overall performance among all settings as it can achieve competitive results on few-shot classes and does not degrade the base class performance at all. Our approach has demonstrated that the long desired never-forgetting learner is available in object detection.

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