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- W3049632937 abstract "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." @default.
- W3049632937 created "2020-08-21" @default.
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- W3049632937 date "2020-08-13" @default.
- W3049632937 modified "2023-10-18" @default.
- W3049632937 title "A Multimodal Late Fusion Model for E-Commerce Product Classification" @default.
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- W3049632937 doi "https://doi.org/10.48550/arxiv.2008.06179" @default.
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