In this paper, we study the problem of object counting with incomplete annotations. Based on the observation that in many object counting problems the target objects are normally repeated and highly similar to each other, we are particularly interested in the setting when only a few exemplar annotations are provided. Directly applying object detection with incomplete annotations will result in severe accuracy degradation due to its improper handling of unlabeled object instances. To address the problem, we propose a positiveness-focused object detector (PFOD) to progressively propagate the incomplete labels before applying the general object detection algorithm. The PFOD focuses on the positive samples and ignore the negative instances at most of the learning time. This strategy, though simple, dramatically boosts the object counting accuracy. On the CARPK dataset for parking lot car counting, we improved mAP@0.5 from 4.58% to 72.44% using only 5 training images each with 5 bounding boxes. On the Drink35 dataset for shelf product counting, the mAP@0.5 is improved from 14.16% to 53.73% using 10 training images each with 5 bounding boxes.
翻译:在本文中,我们用不完整的注释来研究天体计数问题。 基于在许多天体计数问题中目标物体通常重复出现,而且彼此非常相似的观察, 我们特别感兴趣的是只提供几个示例说明的设置。 直接应用不完整的说明来进行天体探测, 将由于对未贴标签物体的处理不当而导致其精确度严重下降。 为了解决这个问题, 我们建议使用一个以积极性为重点的天体探测器(PFOD), 在应用普通天体探测算法之前, 逐步传播不完整的标签。 PFOD 将焦点放在正样上, 在大多数学习时间忽略负面的事例。 这个策略虽然简单, 极大地提高了天体计数的准确性。 在 CARPK 的停车场计票数据集中, 我们只用5.58%到72.44%的5个装箱的培训图像改进了 mAP@0.5。 关于储存产品计数的饮料数据集, mAP@0.5在14.16%到53.73%之间, 使用10个装5个装箱的训练图。