Few-Shot Object Counting and Detection
Thanh Nguyen, Chau Pham, Khoi Nguyen, Minh Hoai
"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 the objects of the target class. This task shares the same supervision as the few-shot 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 taking 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 a two new datasets named FSCD-147 and FSCD-LVIS. Both datasets contain images with complex scene, multiple object classes per image, and huge variation in object shapes, sizes, and appearance. Our propose 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."