Methods › Natural Language Processing › Language Model Pre-Training › K3M
K3M
Introduced by Yushan Zhu et al. in Knowledge Perceived Multi-modal Pretraining in E-commerce
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
K3M is a multi-modal pretraining method for e-commerce product data that introduces knowledge modality to correct the noise and supplement the missing of image and text modalities. The modal-encoding layer extracts the features of each modality. The modal-interaction layer is capable of effectively modeling the interaction of multiple modalities, where an initial-interactive feature fusion model is designed to maintain the independence of image modality and text modality, and a structure aggregation module is designed to fuse the information of image, text, and knowledge modalities. K3M is pre-trained with three pretraining tasks, including masked object modeling (MOM), masked language modeling (MLM), and link prediction modeling (LPM).
Papers archive 2025-07-28
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Knowledge Perceived Multi-modal Pretraining in E-commerce 20 Aug 2021 · 1 repository · arXiv:2109.00895Syntology ran 1 of 1 samples · 0 unverified
Tasks archive 2025-07-28
4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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