论文标题
几个射击对象计数和检测
Few-shot Object Counting and Detection
论文作者
论文摘要
我们解决了一项新的任务，即对象计数和检测。给定目标对象类的一些示例边界框，我们试图计数和检测目标类的所有对象。此任务与几个弹出对象计数相同的监督，但另外还输出对象边界框以及总体计数。为了解决这个具有挑战性的问题，我们介绍了一种新颖的两阶段训练策略和一种新颖的不确定性 - 少数光对象探测器：计数 - 滴定。前者的目的是生成伪基真正的边界框来训练后者。后者利用了前者提供的伪基真实，但采取了必要的步骤来解释伪基真实的不完美。为了验证我们在新任务上的方法的性能，我们介绍了两个名为FSCD-147和FSCD-LVIS的新数据集。这两个数据集都包含具有复杂场景的图像，每个图像多个对象类以及对象形状，大小和外观的巨大变化。我们提出的方法优于非常强大的基线，该基线是根据数量数量计数和少量对象检测而适应的，并且在计数和检测指标中都有很大的余量。代码和模型可在https://github.com/vinairesearch/counting-detr上找到。
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.
