DiffusionMTL: Learning Multi-Task Denoising Diffusion Model from Partially Annotated Data

arXiv:2403.15389v1 Announce Type: cross
Abstract: 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 …

annotated data arxiv cs.cv cs.lg data denoising diffusion diffusion model type