Papers › MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning
MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning
Simon Vandenhende, Stamatios Georgoulis, Luc van Gool
In this paper, we argue about the importance of considering task interactions at multiple scales when distilling task information in a multi-task learning setup. In contrast to common belief, we show that tasks with high affinity at a certain scale are not guaranteed to retain this behaviour at other scales, and vice versa. We propose a novel architecture, namely MTI-Net, that builds upon this finding in three ways. First, it explicitly models task interactions at every scale via a multi-scale multi-modal distillation unit. Second, it propagates distilled task information from lower to higher scales via a feature propagation module. Third, it aggregates the refined task features from all scales via a feature aggregation unit to produce the final per-task predictions. Extensive experiments on two multi-task dense labeling datasets show that, unlike prior work, our multi-task model delivers on the full potential of multi-task learning, that is, smaller memory footprint, reduced number of calculations, and better performance w.r.t. single-task learning. The code is made publicly available: https://github.com/SimonVandenhende/Multi-Task-Learning-PyTorch.
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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 |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | NYU Depth v2 | MTI-Net (HRNet-48) | Mean IoU | 49.0 | #73 of 121 | Archive leaderboard | report |
| Semantic Segmentation | UrbanLF | MTINet (HRNetV2-W48) | mIoU (Real) | n.a. | #7 of 14 | Archive leaderboard | report |
| Semantic Segmentation | UrbanLF | MTINet (HRNetV2-W48) | mIoU (Syn) | 79.10 | #7 of 14 | 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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