We present a framework for efficient inference in structured image models
that explicitly reason about objects. We achieve this by performing
probabilistic inference using a recurrent neural network that attends to scene
elements and processes them one at a time. Crucially, the model itself learns
to choose the appropriate number of inference steps. We use this scheme to
learn to perform inference in partially specified 2D models (variable-sized
variational auto-encoders) and fully specified 3D models (probabilistic
renderers). We show that such models learn to identify multiple objects -
counting, locating and classifying the elements of a scene - without any
supervision, e.g., decomposing 3D images with various numbers of objects in a
single forward pass of a neural network. We further show that the networks
produce accurate inferences when compared to supervised counterparts, and that
their structure leads to improved generalization.

提出了一种针对结构化图像模型进行高效推断的框架，可以通过使用循环神经网络执行概率推断实现对场景元素进行有意识的处理，从而在部分特定的 2D 模型（大小可变的变分自动编码器）和完全指定的 3D 模型（概率渲染器）上进行推断而不需要监督学习，从而实现在单次正向神经网络传递中对包括多个对象的 3D 图像进行分解、计数、定位和分类等功能。同时，该文研究还表明，相比较于监督学习，采用该网络结构可以产生更加准确的推断，并且具有良好的泛化能力。