Author
Kınlı, Osman Furkan, Özcan, Barış, Kıraç, Mustafa Furkan
Publication Date
2019
Publication Place
-
IEEE
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
978-1-7281-5023-9
Record ID
300972c8-03e9-4877-8a32-dda455a5d956
Library Location
Computer Science
Date
2019
Sample Text
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.
DOI
10.1109/ICCVW.2019.00376
