Incremental Few-Shot Object Detection for Robotics

• URL: http://arxiv.org/abs/2005.02641v2
• Date: Wed, 23 Mar 2022 09:05:22 GMT
• Title: Incremental Few-Shot Object Detection for Robotics
• Authors: Yiting Li, Haiyue Zhu, Sichao Tian, Fan Feng, Jun Ma, Chek Sing Teo, Cheng Xiang, Prahlad Vadakkepat, Tong Heng Lee
• Abstract summary: Class-Incremental Few-Shot Object Detection (CI-FSOD) framework enables deep object detection network to perform effective continual learning from just few-shot samples. Our framework is simple yet effective and outperforms the previous SOTA with a significant margin of 2.4 points in AP performance.
• Score: 15.082365880914896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Incremental few-shot learning is highly expected for practical robotics applications. On one hand, robot is desired to learn new tasks quickly and flexibly using only few annotated training samples; on the other hand, such new additional tasks should be learned in a continuous and incremental manner without forgetting the previous learned knowledge dramatically. In this work, we propose a novel Class-Incremental Few-Shot Object Detection (CI-FSOD) framework that enables deep object detection network to perform effective continual learning from just few-shot samples without re-accessing the previous training data. We achieve this by equipping the widely-used Faster-RCNN detector with three elegant components. Firstly, to best preserve performance on the pre-trained base classes, we propose a novel Dual-Embedding-Space (DES) architecture which decouples the representation learning of base and novel categories into different spaces. Secondly, to mitigate the catastrophic forgetting on the accumulated novel classes, we propose a Sequential Model Fusion (SMF) method, which is able to achieve long-term memory without additional storage cost. Thirdly, to promote inter-task class separation in feature space, we propose a novel regularization technique that extends the classification boundary further away from the previous classes to avoid misclassification. Overall, our framework is simple yet effective and outperforms the previous SOTA with a significant margin of 2.4 points in AP performance.

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