Relatable Clothing: Soft-Attention Mechanism for Detecting Worn/Unworn Objects
Thomas Truong, Svetlana Yanushkevich
IEEE Access · 2021
doi:10.1109/access.2021.3101789 (opens in a new tab)
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Abstract
We have identified a need in visual relationship detection and biometrics related research for a dataset and model which focuses on person-clothing pairs. Previous to our Relatable Clothing dataset, there were no publicly available datasets usable for “worn” and “unworn” clothing detection. In this paper we propose a novel visual relationship model architecture for “worn” and “unworn” clothing detection that makes use of a soft attention mechanism for feature fusion between a conventional ResNet backbone and our novel person-clothing mask feature extraction architecture. The best proposed model achieves 98.62% accuracy, 99.50% precision, 98.31% recall, and 99.14% specificity on the Relatable Clothing dataset, outperforming our previous iterations. We release our models which can be found on the Relatable Clothing GitHub repository (<uri>https://github.com/th-truong/relatable_clothing</uri>) for future research and applications into detecting and analyzing person-clothing pairs.
Abstract licence: CC BY-NC-ND
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