Few-shot Object Counting and Detection

                                                            @InProceedings{10.1007/978-3-031-20044-1_20,
author=”Nguyen, Thanh
and Pham, Chau
and Nguyen, Khoi
and Hoai, Minh”,
editor=”Avidan, Shai
and Brostow, Gabriel
and Ciss{\’e}, Moustapha
and Farinella, Giovanni Maria
and Hassner, Tal”,
title=”Few-Shot Object Counting and Detection”,
booktitle=”Computer Vision — ECCV 2022″,
year=”2022″,
publisher=”Springer Nature Switzerland”,
address=”Cham”,
pages=”348–365″,
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 https://github.com/VinAIResearch/Counting-DETR.”,
isbn=”978-3-031-20044-1″
}