In this paper, we present a method to learn a visual representation adapted for e-commerce products. Based on weakly supervised learning, our model learns from noisy datasets crawled on e-commerce website catalogs and does not require any manual labeling. We show that our representation can be used for downward classification tasks over clothing categories with different levels of granularity. We also demonstrate that the learnt representation is suitable for image retrieval. We achieve nearly state-of-art results on the DeepFashion In-Shop Clothes Retrieval and Categories Attributes Prediction [12] tasks, without using the provided training set.
REF crawled the internet to gather a dataset from fashion e-commerce websites to perform weakly supervised image retrieval and tagging.
Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction
2017 IEEE International Conference on Computer Vision Workshop (ICCVW)
Computer Science