  Different from shopping in physical stores, where people have the opportunity
to closely check a product (e.g., touching the surface of a T-shirt or smelling
the scent of perfume) before making a purchase decision, online shoppers rely
greatly on the uploaded product images to make any purchase decision. The
decision-making is challenging when selling or purchasing second-hand items
online since estimating the items' prices is not trivial. In this work, we
present a vision-based price suggestion system for the online second-hand item
shopping platform. The goal of vision-based price suggestion is to help sellers
set effective prices for their second-hand listings with the images uploaded to
the online platforms.
  First, we propose to better extract representative visual features from the
images with the aid of some other image-based item information (e.g., category,
brand). Then, we design a vision-based price suggestion module which takes the
extracted visual features along with some statistical item features from the
shopping platform as the inputs to determine whether an uploaded item image is
qualified for price suggestion by a binary classification model, and provide
price suggestions for items with qualified images by a regression model.
According to two demands from the platform, two different objective functions
are proposed to jointly optimize the classification model and the regression
model. For better model training, we also propose a warm-up training strategy
for the joint optimization. Extensive experiments on a large real-world dataset
demonstrate the effectiveness of our vision-based price prediction system.
