在他们的新论文中，研究者指出：他们给系统添加越来越多的物体的识别能力时，单个物体的平均部件数量随之越来越少。

In their new paper, the researchers show that, as they add the ability to recognize more objects to their system, the average number of parts per object steadily declines.

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传统的物像识别系统在辨别数字图形中的一个特定物体时，通常是从寻找该物体的显著部件开始的。

A conventional object recognition system, when trying to discern a particular type of object in a digital image, will generally begin by looking for the object's salient features.

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在默认的规则集中，可以通过使用字典分类令牌来识别部件类型。

In the default rule set, a part type is identified by classifying the token using a dictionary.

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