  With the prosperity of e-commerce industry, various modalities, e.g., vision
and language, are utilized to describe product items. It is an enormous
challenge to understand such diversified data, especially via extracting the
attribute-value pairs in text sequences with the aid of helpful image regions.
Although a series of previous works have been dedicated to this task, there
remain seldomly investigated obstacles that hinder further improvements: 1)
Parameters from up-stream single-modal pretraining are inadequately applied,
without proper jointly fine-tuning in a down-stream multi-modal task. 2) To
select descriptive parts of images, a simple late fusion is widely applied,
regardless of priori knowledge that language-related information should be
encoded into a common linguistic embedding space by stronger encoders. 3) Due
to diversity across products, their attribute sets tend to vary greatly, but
current approaches predict with an unnecessary maximal range and lead to more
potential false positives. To address these issues, we propose in this paper a
novel approach to boost multi-modal e-commerce attribute value extraction via
unified learning scheme and dynamic range minimization: 1) Firstly, a unified
scheme is designed to jointly train a multi-modal task with pretrained
single-modal parameters. 2) Secondly, a text-guided information range
minimization method is proposed to adaptively encode descriptive parts of each
modality into an identical space with a powerful pretrained linguistic model.
3) Moreover, a prototype-guided attribute range minimization method is proposed
to first determine the proper attribute set of the current product, and then
select prototypes to guide the prediction of the chosen attributes. Experiments
on the popular multi-modal e-commerce benchmarks show that our approach
achieves superior performance over the other state-of-the-art techniques.
