Weakly Supervised Learning for Depth Estimation in Monocular Images

利用两种类型的机器学习方法，即学习排序和超集学习，该项目开发了单眼图像深度估计的新方法。这两种方法都只需要“弱”监督，或者以相对形式（“物体B在物体A后面”），或者以粗略的绝对深度信息（“物体A靠近相机”）的形式，从而促进训练数据的获取。作为预测，它们以排名的形式生成定性深度图，指定场景中物体的相对顺序。这与基于统计回归的传统方法相反，后者需要精确的训练数据并产生（不必要的）精确预测。对于这两种方法，将开发专门针对深度估计问题的机器学习算法。这些将与两种特征构建方法相结合：对人类感知的单目深度线索进行系统（手工制作）建模，以及使用深度神经网络进行表征学习。我们的定性、弱监督的单目深度估计方法将相互分析和比较，以及与现有的基于统计回归的方法进行比较。最后但并非最不重要的是，我们的新算法的好处将在几个重要的应用中进行研究，即图像和视频中的视觉概念分类，视觉概念检测（通过定位）和图像分割。

Leveraging two types of machine learning methods, namely learning to rank and superset learning, this project develops novel approaches to depth estimation in monocular images. Both approaches merely require "weak" supervision, either in the form of relative ("object B is behind object A") or rough absolute depth information ("object A is close to the camera"), and thereby facilitate the acquisition of training data. As predictions, they produce qualitative depth maps in the form of rankings, specifying the relative order of objects in a scene. This is in contrast to conventional approaches based on statistical regression, which require precise training data and produce (unnecessarily) precise predictions. For both approaches, machine learning algorithms specifically tailored for the problem of depth estimation will be developed. These will be combined with two approaches to feature construction: the systematic (hand-crafted) modeling of monocular depth clues of human perception, and the use of deep neural networks for representation learning. Our qualitative, weakly supervised approaches to monocular depth estimation will be analyzed and compared with each other, as well as with existing approaches based on statistical regression. Last but not least, the benefits of our new algorithms will be investigated for several important applications, namely visual concept classification in images and videos, visual concept detection (by means of localization), and image segmentation.