Papers › MUTAN: Multimodal Tucker Fusion for Visual Question Answering
MUTAN: Multimodal Tucker Fusion for Visual Question Answering
Hedi Ben-Younes, Rémi Cadene, Matthieu Cord, Nicolas Thome
Bilinear models provide an appealing framework for mixing and merging information in Visual Question Answering (VQA) tasks. They help to learn high level associations between question meaning and visual concepts in the image, but they suffer from huge dimensionality issues. We introduce MUTAN, a multimodal tensor-based Tucker decomposition to efficiently parametrize bilinear interactions between visual and textual representations. Additionally to the Tucker framework, we design a low-rank matrix-based decomposition to explicitly constrain the interaction rank. With MUTAN, we control the complexity of the merging scheme while keeping nice interpretable fusion relations. We show how our MUTAN model generalizes some of the latest VQA architectures, providing state-of-the-art results.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Visual Question Answering (VQA) | VQA v2 test-dev | MUTAN | Accuracy | 67.42 | #39 of 56 | Archive leaderboard | report |
| Visual Question Answering (VQA) | VQA v2 test-std | MUTAN | overall | 67.4 | #34 of 38 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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