We present an object representation, called \textbf{Dense RepPoints}, for flexible and detailed modeling of object appearance and geometry. In contrast to the coarse geometric localization and feature extraction of bounding boxes, Dense RepPoints adaptively distributes a dense set of points to semantically and geometrically significant positions on an object, providing informative cues for object analysis. Techniques are developed to address challenges related to supervised training for dense point sets from image segments annotations and making this extensive representation computationally practical. In addition, the versatility of this representation is exploited to model object structure over multiple levels of granularity. Dense RepPoints significantly improves performance on geometrically-oriented visual understanding tasks, including a $1.6$ AP gain in object detection on the challenging COCO benchmark.
翻译:我们提出一个名为\textbf{Dense Reppoints}的物体示意图,以灵活和详细地模拟物体外观和几何。与捆绑盒粗略的几何定位和特征提取相比,登斯Reppoints适应性地将一组密集点分布到物体上的具有地震和几何重要性的位置,为物体分析提供信息提示。开发了各种技术,以应对与从图像部分说明对稠密点数据集进行监督培训有关的挑战,并使这种广泛的代表性具有计算实用性。此外,这种代表方式的多功能被利用来模拟多个颗粒度以上的物体结构。登斯Reppoints大大改进了以几何计量为导向的视觉理解任务的性能,包括在具有挑战性的COCO基准上的物体探测方面获得1.6美元的AP收益。