Papers › M&M Mix: A Multimodal Multiview Transformer Ensemble
M&M Mix: A Multimodal Multiview Transformer Ensemble
Xuehan Xiong, Anurag Arnab, Arsha Nagrani, Cordelia Schmid
This report describes the approach behind our winning solution to the 2022 Epic-Kitchens Action Recognition Challenge. Our approach builds upon our recent work, Multiview Transformer for Video Recognition (MTV), and adapts it to multimodal inputs. Our final submission consists of an ensemble of Multimodal MTV (M&M) models varying backbone sizes and input modalities. Our approach achieved 52.8% Top-1 accuracy on the test set in action classes, which is 4.1% higher than last year's winning entry.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Action Recognition | EPIC-KITCHENS-100 | M&M (WTS 60M) | Action@1 | 53.6 | #4 of 32 | Archive leaderboard | report |
| Action Recognition | EPIC-KITCHENS-100 | M&M (WTS 60M) | Noun@1 | 66.3 | #4 of 32 | Archive leaderboard | report |
| Action Recognition | EPIC-KITCHENS-100 | M&M (WTS 60M) | Verb@1 | 72.0 | #4 of 32 | 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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