CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection

• URL: http://arxiv.org/abs/2204.05220v1
• Date: Mon, 11 Apr 2022 16:04:54 GMT
• Title: CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection
• Authors: Karim Guirguis, Ahmed Hendawy, George Eskandar, Mohamed Abdelsamad, Matthias Kayser, Juergen Beyerer
• Abstract summary: Generalized few-shot object detection (G-FSOD) aims to tackle FSOD without forgetting previously seen base classes. CFA adapts a continual learning method, namely Average Gradient Episodic Memory (A-GEM) to G-FSOD. Our method outperforms current FSOD and G-FSOD approaches on the novel task with minor degeneration on the base task.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot object detection (FSOD) seeks to detect novel categories with limited data by leveraging prior knowledge from abundant base data. Generalized few-shot object detection (G-FSOD) aims to tackle FSOD without forgetting previously seen base classes and, thus, accounts for a more realistic scenario, where both classes are encountered during test time. While current FSOD methods suffer from catastrophic forgetting, G-FSOD addresses this limitation yet exhibits a performance drop on novel tasks compared to the state-of-the-art FSOD. In this work, we propose a constraint-based finetuning approach (CFA) to alleviate catastrophic forgetting, while achieving competitive results on the novel task without increasing the model capacity. CFA adapts a continual learning method, namely Average Gradient Episodic Memory (A-GEM) to G-FSOD. Specifically, more constraints on the gradient search strategy are imposed from which a new gradient update rule is derived, allowing for better knowledge exchange between base and novel classes. To evaluate our method, we conduct extensive experiments on MS-COCO and PASCAL-VOC datasets. Our method outperforms current FSOD and G-FSOD approaches on the novel task with minor degeneration on the base task. Moreover, CFA is orthogonal to FSOD approaches and operates as a plug-and-play module without increasing the model capacity or inference time.

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