ANR DEEPVISION: Seeing and understanding humans with deep structured models

计算机视觉的进步使几年前似乎不可能实现的应用成为可能，例如实时手势识别、移动设备上的人脸检测和环境的3D映射。机器学习是这一发展背后的主要推动力。与视觉信息相对应的海量数据，以及由于不同的视点、形状等而固有的巨大的可变性，使得学习成为一种吸引人的方法。然而，当前的技术状态受到限制。结构关系使许多现实情况超出了商业应用的范围，例如人与人和人与物的交互，长期的动态行为，以及高度变形和关节对象的识别。我们建议通过创建深度结构模型来解决这些挑战，深度学习是一种强大的模型和算法家族，而结构化模型非常适合于对复杂数据进行建模。

Advances in computer vision have enabled applications that seemed impossible some years ago, e.g. gesture recognition in real time, face detection on mobile devices and 3d mapping of environments. Machine learning is a major driving force behind this development. The vast amount of data corresponding to visual information, as well as its inherently large variability due to different viewpoints, shapes, etc. make learning an appealing approach.However, the current state of the art suffers from limitations. Structural relationships put many realistic situations out of reach of commercial applications, such as person-person and person-object interactions, long-term dynamical behaviour, and the recognition of highly deformable and articulated objects.We propose to address these challenges by creating deep structured models, combining the advantages of deep learning, a powerful family of models and algorithms, with structured models, which are well-suited for modeling complex data.