扩散模型通过迭代计算提升视觉感知任务表现，更省数据与算力。
Scaling Properties of Diffusion Models for Perceptual Tasks
- 将深度估计等任务统一为图像到图像翻译，利用迭代推断提升效果。
- 在少数据、低算力下达到顶尖方法水平，验证了缩放效应。
- 适合追求高效高精度视觉感知的开发者和研究者使用。
本文提出，扩散模型的迭代计算机制不仅适用于生成任务，也能有效提升视觉感知任务性能。我们统一深度估计、光流预测和非可见部分分割等任务为图像到图像翻译框架，并系统分析其训练与推理时计算量扩展的规律。基于此，构建出最优的训练与推理策略，使模型在显著减少数据与算力投入的情况下，仍可达到当前最佳性能。相关代码与模型已开源。
原文摘要 · Abstract (English)
In this paper, we argue that iterative computation with diffusion models offers a powerful paradigm for not only generation but also visual perception tasks. We unify tasks such as depth estimation, optical flow, and amodal segmentation under the framework of image-to-image translation, and show how diffusion models benefit from scaling training and test-time compute for these perceptual tasks. Through a careful analysis of these scaling properties, we formulate compute-optimal training and inference recipes to scale diffusion models for visual perception tasks. Our models achieve competitive performance to state-of-the-art methods using significantly less data and compute. To access our code and models, see https://scaling-diffusion-perception.github.io .
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