PT Journal
AU Vacit Oguz Yazici
   Long Long Yu
   Arnau Ramisa
   Luis Herranz
   Joost Van de Weijer
TI Main Product Detection with Graph Networks for Fashion
SO Multimedia Tools and Applications
JI MTAP
PY 2022
DI 10.1007/s11042-022-13572-x
AB 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.
ER