Methods › General › Semi-Supervised Learning Methods › M3L
Multi-modal Teacher for Masked Modality Learning
M3L
Introduced by Harsh Maheshwari et al. in Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
The archive carries no description for this method.
Papers archive 2025-07-28
2 shown of 2, 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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The Power of the Senses: Generalizable Manipulation from Vision and Touch through Masked Multimodal Learning 2 Nov 2023 · 0 repositories · arXiv:2311.00924
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Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation 21 Apr 2023 · 1 repository · arXiv:2304.10756
Tasks archive 2025-07-28
6 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 |
|---|---|
| RGBD Semantic Segmentation | 1 |
| Robust Semi-Supervised RGBD Semantic Segmentation | 1 |
| Segmentation | 1 |
| Semantic Segmentation | 1 |
| Semi-Supervised RGBD Semantic Segmentation | 1 |
| Semi-Supervised Semantic Segmentation | 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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections