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 of 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% ± 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".