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Research on automated, image based identification of clothing categories andfashion landmarks has recently gained significant interest due to its potentialimpact on areas such as robotic clothing manipulation, automated clothessorting and recycling, and online shopping. Several public and annotatedfashion datasets have been created to facilitate research advances in thisdirection. In this work, we make the first step towards leveraging the data andtechniques developed for fashion image analysis in vision-based roboticclothing manipulation tasks. We focus on techniques that can generalize fromlarge-scale fashion datasets to less structured, small datasets collected in arobotic lab. Specifically, we propose training data augmentation methods suchas elastic warping, and model adjustments such as rotation invariantconvolutions to make the model generalize better. Our experiments demonstratethat our approach outperforms stateof-the art models with respect to clothingcategory classification and fashion landmark detection when tested onpreviously unseen datasets. Furthermore, we present experimental results on anew dataset composed of images where a robot holds different garments,collected in our lab.

Fashion Landmark Detection and Category Classification for Robotics