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 fine-grained 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 locate multiple object parts and a two-stream classification network that en- codes object-level and part-level cues simultaneously. By adopting a set of sharing strategies between the computation of multiple object parts, the proposed architecture is very efficient running at 20 frames/sec during inference. Experimental results on the CUB-200-2011 dataset reveal the effectiveness of the proposed architecture, from both the perspective of classification accuracy and model interpretability.

本文提出了一种基于部位的CNN模型，在手动标注的强部位注释的基础上，通过模拟物体部件之间微小的差异来显式解释精细分类过程，同时由计算多个物体部分之间的共享策略，提高了效率，并获得了良好的准确度和模型可解释性的实验结果。

用Part-Stacked CNN进行细粒度视觉分类