Papers › A Visual Attention Grounding Neural Model for Multimodal Machine Translation
A Visual Attention Grounding Neural Model for Multimodal Machine Translation
Mingyang Zhou, Runxiang Cheng, Yong Jae Lee, Zhou Yu
We introduce a novel multimodal machine translation model that utilizes parallel visual and textual information. Our model jointly optimizes the learning of a shared visual-language embedding and a translator. The model leverages a visual attention grounding mechanism that links the visual semantics with the corresponding textual semantics. Our approach achieves competitive state-of-the-art results on the Multi30K and the Ambiguous COCO datasets. We also collected a new multilingual multimodal product description dataset to simulate a real-world international online shopping scenario. On this dataset, our visual attention grounding model outperforms other methods by a large margin.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multimodal Machine Translation | Multi30K | VAG-NMT | BLEU (EN-DE) | 31.6 | #12 of 15 | Archive leaderboard | report |
| Multimodal Machine Translation | Multi30K | VAG-NMT | Meteor (EN-DE) | 52.2 | #12 of 15 | Archive leaderboard | report |
| Multimodal Machine Translation | Multi30K | VAG-NMT | Meteor (EN-FR) | 70.3 | #12 of 15 | Archive leaderboard | report |
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