Main product detection with graph networks for fashion

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 on 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.

Automated summary

Computer vision has made progress in the online fashion retail industry by proposing a model that utilizes Graph Convolutional Networks (GCN) to detect fashion products in boundary boxes. Compared to the state-of-the-art approach, this method performs better in scenarios where title-input is missing and during cross-dataset evaluation.