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In the context of fine-grained visual categorization, the ability to interpret models as human-understandable visual manuals is sometimes as important as achieving high classification accuracy. In this paper, we propose a novel Part-Stacked CNN architecture that explicitly explains the finegrained recognition process by modeling subtle differences from object parts. Based on manually-labeled strong part annotations, the proposed architecture consists of a fully convolutional network to locatedoi:10.1109/cvpr.2016.132 dblp:conf/cvpr/HuangXTZ16 fatcat:bc2zb23bpzbhrf3wq47r5mtlha