Methods › Computer Vision › Vision and Language Pre-Trained Models › InterBERT
InterBERT
Introduced by Junyang Lin et al. in InterBERT: Vision-and-Language Interaction for Multi-modal Pretraining
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
InterBERT aims to model interaction between information flows pertaining to different modalities. This new architecture builds multi-modal interaction and preserves the independence of single modal representation. InterBERT is built with an image embedding layer, a text embedding layer, a single-stream interaction module, and a two stream extraction module. The model is pre-trained with three tasks: 1) masked segment modeling, 2) masked region modeling, and 3) image-text matching.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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InterBERT: Vision-and-Language Interaction for Multi-modal Pretraining 30 Mar 2020 · 0 repositories · arXiv:2003.13198
Tasks archive 2025-07-28
5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Image Retrieval | 1 |
| Image-text matching | 1 |
| Retrieval | 1 |
| Text Matching | 1 |
| Visual Commonsense Reasoning | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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