Relatable Clothing: Detecting Visual Relationships between People and Clothing
Detecting visual relationships between people and clothing in an image has been a relatively unexplored problem in the field of computer vision and biometrics. The lack readily available public dataset for `worn'' and unworn'' classification has slowed the development of solutions for this problem. We present the release of the Relatable Clothing Dataset which contains 35287 person-clothing pairs and segmentation masks for the development of worn'' and unworn'' classification models. Additionally, we propose a novel soft attention unit for performing worn'' and unworn'' classification using deep neural networks. The proposed soft attention models have an accuracy of upward $98.55\% \pm 0.35\%$ on the Relatable Clothing Dataset and demonstrate high generalizable, allowing us to classify unseen articles of clothing such as high visibility vests as worn'' or `unworn''.
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