  Recently, there has been an increased interest in the practical problem of
learning multiple dense scene understanding tasks from partially annotated
data, where each training sample is only labeled for a subset of the tasks. The
missing of task labels in training leads to low-quality and noisy predictions,
as can be observed from state-of-the-art methods. To tackle this issue, we
reformulate the partially-labeled multi-task dense prediction as a pixel-level
denoising problem, and propose a novel multi-task denoising diffusion framework
coined as DiffusionMTL. It designs a joint diffusion and denoising paradigm to
model a potential noisy distribution in the task prediction or feature maps and
generate rectified outputs for different tasks. To exploit multi-task
consistency in denoising, we further introduce a Multi-Task Conditioning
strategy, which can implicitly utilize the complementary nature of the tasks to
help learn the unlabeled tasks, leading to an improvement in the denoising
performance of the different tasks. Extensive quantitative and qualitative
experiments demonstrate that the proposed multi-task denoising diffusion model
can significantly improve multi-task prediction maps, and outperform the
state-of-the-art methods on three challenging multi-task benchmarks, under two
different partial-labeling evaluation settings. The code is available at
https://prismformore.github.io/diffusionmtl/.
