  In this study, we investigate in-shop clothing retrieval performance of
densely-connected Capsule Networks with dynamic routing. To achieve this, we
propose Triplet-based design of Capsule Network architecture with two different
feature extraction methods. In our design, Stacked-convolutional (SC) and
Residual-connected (RC) blocks are used to form the input of capsule layers.
Experimental results show that both of our designs outperform all variants of
the baseline study, namely FashionNet, without relying on the landmark
information. Moreover, when compared to the SOTA architectures on clothing
retrieval, our proposed Triplet Capsule Networks achieve comparable recall
rates only with half of parameters used in the SOTA architectures.
