The Impact of Background Removal on Performance of Neural Networks for
  Fashion Image Classification and Segmentation
Junhui Liang Ying Liu Vladimir Vlassov
Fashion understanding is a hot topic in computer vision, with many
applications having great business value in the market. Fashion understanding
remains a difficult challenge for computer vision due to the immense diversity
of garments and various scenes and backgrounds. In this work, we try removing
the background from fashion images to boost data quality and increase model
performance. Having fashion images of evident persons in fully visible
garments, we can utilize Salient Object Detection to achieve the background
removal of fashion data to our expectations. A fashion image with the
background removed is claimed as the "rembg" image, contrasting with the
original one in the fashion dataset. We conducted extensive comparative
experiments with these two types of images on multiple aspects of model
training, including model architectures, model initialization, compatibility
with other training tricks and data augmentations, and target task types. Our
experiments show that background removal can effectively work for fashion data
in simple and shallow networks that are not susceptible to overfitting. It can
improve model accuracy by up to 5% in the classification on the FashionStyle14
dataset when training models from scratch. However, background removal does not
perform well in deep neural networks due to incompatibility with other
regularization techniques like batch normalization, pre-trained initialization,
and data augmentations introducing randomness. The loss of background pixels
invalidates many existing training tricks in the model training, adding the
risk of overfitting for deep models.