We present Supervision by Registration and Triangulation (SRT), an unsupervised approach that utilizes unlabeled multi-view video to improve the accuracy and precision of landmark detectors. Being able to utilize unlabeled data enables our detectors to learn from massive amounts of unlabeled data freely available and not be limited by the quality and quantity of manual human annotations. To utilize unlabeled data, there are two key observations: (1) the detections of the same landmark in adjacent frames should be coherent with registration, i.e., optical flow. (2) the detections of the same landmark in multiple synchronized and geometrically calibrated views should correspond to a single 3D point, i.e., multi-view consistency. Registration and multi-view consistency are sources of supervision that do not require manual labeling, thus it can be leveraged to augment existing training data during detector training. End-to-end training is made possible by differentiable registration and 3D triangulation modules. Experiments with 11 datasets and a newly proposed metric to measure precision demonstrate accuracy and precision improvements in landmark detection on both images and video. Code is available at https://github.com/D-X-Y/landmark-detection.

翻译：我们提出注册和三角监督(SRT),这是使用未贴标签的多视图视频来提高里程碑探测器的准确性和准确性的一种不受监督的方法。能够使用未贴标签的数据使我们的探测器能够从大量可免费获取的未贴标签数据中学习,而不受人工人工说明的质量和数量的限制。为了使用未贴标签的数据,我们提出两项关键意见:(1)在相邻框架内同一标志的探测应当与登记(即光学流)保持一致。(2)在多个同步和几何校准的视图中,同一标志的探测应当对应一个单一的3D点,即多视图一致性。登记和多视图一致性是不需要人工标签的监督来源,因此可以在检测培训期间用来加强现有的培训数据。通过不同的登记和3D三角定位模块进行端培训。用11个数据集进行实验,新提出的测量指标显示在图像和视频的标志性探测方面准确性和精确性改进。https://githtub./Ddection-D代码可在https://Xdection-mart-D。