Object Permanence allows people to reason about the location of non-visible
objects, by understanding that they continue to exist even when not perceived
directly. Object Permanence is critical for building a model of the world,
since objects in natural visual scenes dynamically occlude and contain
each-other. Intensive studies in developmental psychology suggest that object
permanence is a challenging task that is learned through extensive experience.
Here we introduce the setup of learning Object Permanence from data. We explain
why this learning problem should be dissected into four components, where
objects are (1) visible, (2) occluded, (3) contained by another object and (4)
carried by a containing object. The fourth subtask, where a target object is
carried by a containing object, is particularly challenging because it requires
a system to reason about a moving location of an invisible object. We then
present a unified deep architecture that learns to predict object location
under these four scenarios. We evaluate the architecture and system on a new
dataset based on CATER, and find that it outperforms previous localization
methods and various baselines.

通过数据学习目标物体的位置推测，需要拆分成四个子任务，其中第四个子任务最具挑战性，因为它需要一个系统来推理不可见物体的移动位置，本文介绍了一个统一的深度架构，可以在四种情况下学习预测物体位置，并在基于 CATER 的新数据集上对其进行评估，发现它优于以前的定位方法和各种基线。

从视频中学习对象的永恒性

Learning Object Permanence from Video

Computer vision has undergone a dramatic revolution in performance, driven in
large part through deep features trained on large-scale supervised datasets.
However, much of these improvements have focused on static image analysis;
video understanding has seen rather modest improvements. Even though new
datasets and spatiotemporal models have been proposed, simple frame-by-frame
classification methods often still remain competitive. We posit that current
video datasets are plagued with implicit biases over scene and object structure
that can dwarf variations in temporal structure. In this work, we build a video
dataset with fully observable and controllable object and scene bias, and which
truly requires spatiotemporal understanding in order to be solved. Our dataset,
named CATER, is rendered synthetically using a library of standard 3D objects,
and tests the ability to recognize compositions of object movements that
require long-term reasoning. In addition to being a challenging dataset, CATER
also provides a plethora of diagnostic tools to analyze modern spatiotemporal
video architectures by being completely observable and controllable. Using
CATER, we provide insights into some of the most recent state of the art deep
video architectures.

本文通过构建名称为 CATER 的视频数据集，从而提供了深入理解深度视频架构的诊断工具。在该数据集中，场景和对象的结构充满着隐含的偏见，这可以掩盖时间结构的变化，因此需要时空理解才能真正解决。