Main Product Detection with Graph Networks for Fashion
This addresses a specific problem in fashion e-commerce by improving product detection accuracy, though it is incremental as it builds on existing graph network approaches.
The paper tackles main product detection in fashion retail by proposing a Graph Convolutional Network model that leverages relations between image regions, outperforming state-of-the-art methods, especially when title input is missing and in cross-dataset evaluations.
Computer vision has established a foothold in the online fashion retail industry. Main product detection is a crucial step of vision-based fashion product feed parsing pipelines, focused in identifying the bounding boxes that contain the product being sold in the gallery of images of the product page. The current state-of-the-art approach does not leverage the relations between regions in the image, and treats images of the same product independently, therefore not fully exploiting visual and product contextual information. In this paper we propose a model that incorporates Graph Convolutional Networks (GCN) that jointly represent all detected bounding boxes in the gallery as nodes. We show that the proposed method is better than the state-of-the-art, especially, when we consider the scenario where title-input is missing at inference time and for cross-dataset evaluation, our method outperforms previous approaches by a large margin.