Relatable Clothing: Detecting Visual Relationships between People and Clothing
This addresses a relatively unexplored problem in computer vision and biometrics for applications like fashion analysis or security, but it is incremental as it builds on existing deep learning methods.
The paper tackled the problem of detecting visual relationships between people and clothing by releasing a new dataset and proposing a soft attention unit for classification, achieving an accuracy of up to 98.55% ± 0.35% on their dataset.
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''.