Few-shot Object Counting and Detection

• URL: http://arxiv.org/abs/2207.10988v1
• Date: Fri, 22 Jul 2022 10:09:18 GMT
• Title: Few-shot Object Counting and Detection
• Authors: Thanh Nguyen, Chau Pham, Khoi Nguyen, Minh Hoai
• Abstract summary: We tackle a new task of few-shot object counting and detection. Given a few exemplar bounding boxes of a target object class, we seek to count and detect all objects of the target class. This task shares the same supervision as the few-shot object counting but additionally outputs the object bounding boxes along with the total object count. We introduce a novel two-stage training strategy and a novel uncertainty-aware few-shot object detector: Counting-DETR.
• Score: 25.61294147822642
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We tackle a new task of few-shot object counting and detection. Given a few exemplar bounding boxes of a target object class, we seek to count and detect all objects of the target class. This task shares the same supervision as the few-shot object counting but additionally outputs the object bounding boxes along with the total object count. To address this challenging problem, we introduce a novel two-stage training strategy and a novel uncertainty-aware few-shot object detector: Counting-DETR. The former is aimed at generating pseudo ground-truth bounding boxes to train the latter. The latter leverages the pseudo ground-truth provided by the former but takes the necessary steps to account for the imperfection of pseudo ground-truth. To validate the performance of our method on the new task, we introduce two new datasets named FSCD-147 and FSCD-LVIS. Both datasets contain images with complex scenes, multiple object classes per image, and a huge variation in object shapes, sizes, and appearance. Our proposed approach outperforms very strong baselines adapted from few-shot object counting and few-shot object detection with a large margin in both counting and detection metrics. The code and models are available at \url{https://github.com/VinAIResearch/Counting-DETR}.

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