Summary of Diffusionmtl: Learning Multi-task Denoising Diffusion Model From Partially Annotated Data, by Hanrong Ye and Dan Xu
DiffusionMTL: Learning Multi-Task Denoising Diffusion Model from Partially Annotated Data
by Hanrong Ye, Dan Xu
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel framework called DiffusionMTL to tackle the problem of learning multiple dense scene understanding tasks from partially annotated data. The missing task labels in training lead to low-quality and noisy predictions, which is a limitation of state-of-the-art methods. By reformulating the problem as a pixel-level denoising problem, the authors design 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. They also introduce a Multi-Task Conditioning strategy that utilizes multi-task consistency to improve denoising performance. The framework is evaluated on three challenging benchmarks under two partial-labeling settings, outperforming state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps solve a problem where we want to learn multiple things about a scene from some examples, but not all of those things are labeled. Right now, the best ways to do this produce bad results because they don’t account for missing labels. The authors come up with a new way to think about this problem by comparing it to denoising images. They create a model that can learn from noisy data and then use that model to improve its performance on the tasks it’s trying to solve. |
Keywords
* Artificial intelligence * Diffusion * Multi task * Scene understanding
