Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning

14 Nov 2016Quanshi ZhangRuiming CaoYing Nian WuSong-Chun Zhu

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... (read more)

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