This paper proposes a learning strategy that extracts object-part concepts
from a pre-trained convolutional neural network (CNN), in an attempt to 1)
explore explicit semantics hidden in CNN units and 2) gradually grow a
semantically interpretable graphical model on the pre-trained CNN for
hierarchical object understanding. Given part annotations on very few (e.g.,
3-12) objects, our method mines certain latent patterns from the pre-trained
CNN and associates them with different semantic parts. We use a four-layer
And-Or graph to organize the mined latent patterns, so as to clarify their
internal semantic hierarchy. Our method is guided by a small number of part
annotations, and it achieves superior performance (about 13%-107% improvement)
in part center prediction on the PASCAL VOC and ImageNet datasets.

该研究提出了一种从预先训练的卷积神经网络中提取对象 - 部分概念的学习策略，通过挖掘预先训练的 CNN 中的潜在模式，并将它们与不同的语义部分关联来逐步在 CNN 上构建一个具有语义解释性的图形模型，以实现层次化的对象理解。