The ability to detect and track objects in the visual world is a crucial
skill for any intelligent agent, as it is a necessary precursor to any
object-level reasoning process. Moreover, it is important that agents learn to
track objects without supervision (i.e. without access to annotated training
videos) since this will allow agents to begin operating in new environments
with minimal human assistance. The task of learning to discover and track
objects in videos, which we call \textit{unsupervised object tracking}, has
grown in prominence in recent years; however, most architectures that address
it still struggle to deal with large scenes containing many objects. In the
current work, we propose an architecture that scales well to the large-scene,
many-object setting by employing spatially invariant computations (convolutions
and spatial attention) and representations (a spatially local object
specification scheme). In a series of experiments, we demonstrate a number of
attractive features of our architecture; most notably, that it outperforms
competing methods at tracking objects in cluttered scenes with many objects,
and that it can generalize well to videos that are larger and/or contain more
objects than videos encountered during training.

提出了一种能够应对大场景和多物体的无监督对象跟踪体系结构，包括空间不变运算（卷积和空间注意）和本地对象表示方案，在多个实验中展现了在有多个物体的混乱场景中跟踪对象比其他方法更好，并且能够很好地推广到训练时未遇到的更大，物体更多的视频中。