  The resale price assessment of secondhand jewelry items relies heavily on the
individual knowledge and skill of domain experts. In this paper, we propose a
methodology for reconstructing an AI system that autonomously assesses the
resale prices of secondhand jewelry items without the need for professional
knowledge. As shown in recent studies on fashion items, multimodal approaches
combining specifications and visual information of items have succeeded in
obtaining fine-grained representations of fashion items, although they
generally apply simple vector operations through a multimodal fusion. We
similarly build a multimodal model using images and attributes of the product
and further employ state-of-the-art multimodal deep neural networks applied in
computer vision to achieve a practical performance level. In addition, we model
the pricing procedure of an expert using iterative co-attention networks in
which the appearance and attributes of the product are carefully and
iteratively observed. Herein, we demonstrate the effectiveness of our model
using a large dataset of secondhand no brand jewelry items received from a
collaborating fashion retailer, and show that the iterative co-attention
process operates effectively in the context of resale price prediction. Our
model architecture is widely applicable to other fashion items where appearance
and specifications are important aspects.
