Semantic object parts can be useful for several visual recognition tasks.
Lately, these tasks have been addressed using Convolutional Neural Networks
(CNN), achieving outstanding results. In this work we study whether CNNs learn
semantic parts in their internal representation. We investigate the responses
of convolutional filters and try to associate their stimuli with semantic
parts. We perform two extensive quantitative analyses. First, we use
ground-truth part bounding-boxes from the PASCAL-Part dataset to determine how
many of those semantic parts emerge in the CNN. We explore this emergence for
different layers, network depths, and supervision levels. Second, we collect
human judgements in order to study what fraction of all filters systematically
fire on any semantic part, even if not annotated in PASCAL-Part. Moreover, we
explore several connections between discriminative power and semantics. We find
out which are the most discriminative filters for object recognition, and
analyze whether they respond to semantic parts or to other image patches. We
also investigate the other direction: we determine which semantic parts are the
most discriminative and whether they correspond to those parts emerging in the
network. This enables to gain an even deeper understanding of the role of
semantic parts in the network.

本文研究了使用卷积神经网络实现视觉对象识别任务时，CNN 是否学习了语义部分的内部表示，结果发现对于对象识别最有辨别力的卷积滤波器对应着某些语义部分。