Interactive Fine-grained Few-shot Detection of Tools*

Abstract

Few-shot object detection is especially interesting for applications with mobile robots and becomes even more challenging when task-related classes are very similar. This work focuses on such a scenario: detecting different types of household and industrial tools. Such tools can be rare and specific and are usually not covered by existing large datasets, except for common ones such as screwdrivers. Additionally, the target classes might change frequently depending on the robot’s missions. Therefore, we propose DE-fine-ViT, a fine-grained few-shot object detection model that does not require fine-tuning. We build our architecture on top of the elaborate DE-ViT model, extending it with specialized components to improve the fine-grained detection capabilities. The user can construct class and part prototypes tailored to the task in an interactive preparation phase. During inference, our proposed reevaluation module leverages the multi-granularity of prototypes for fine-grained class differentiation. We evaluate our model in multiple realistic experiments, including a specifically created fine-grained dataset, demonstrating its efficacy and suitability for scenarios with little data and low inter-class variance.