The cataloging of product listings is a fundamental problem for most
e-commerce platforms. Despite promising results obtained by unimodal-based
methods, it can be expected that their performance can be further boosted by
the consideration of multimodal product information. In this study, we
investigated a multimodal late fusion approach based on text and image
modalities to categorize e-commerce products on Rakuten. Specifically, we
developed modal specific state-of-the-art deep neural networks for each input
modal, and then fused them at the decision level. Experimental results on
Multimodal Product Classification Task of SIGIR 2020 E-Commerce Workshop Data
Challenge demonstrate the superiority and effectiveness of our proposed method
compared with unimodal and other multimodal methods. Our team named pa_curis
won the 1st place with a macro-F1 of 0.9144 on the final leaderboard.

本研究基于文本和图像模态探讨了一种基于多模态晚期融合的方法，以对 Rakuten 上的电子商务产品进行分类，并证明了该方法相比于单模态和其他多模态方法的有效性和优越性。该研究团队在 SIGIR 2020 电子商务研讨会数据挑战赛的多模态产品分类任务中以 0.9144 的 macro-F1 得分荣获第一名。