Scaling Properties of Diffusion Models for Perceptual Tasks
Audio is streamed directly from the publisher (media.transistor.fm) as published in their RSS feed. Play Podcasts does not host this file. Rights-holders can request removal through the copyright & takedown page.
Show Notes
🤗 Paper Upvotes: 7 | cs.CV, cs.AI
Authors:
Rahul Ravishankar, Zeeshan Patel, Jathushan Rajasegaran, Jitendra Malik
Title:
Scaling Properties of Diffusion Models for Perceptual Tasks
Arxiv:
http://arxiv.org/abs/2411.08034v2
Abstract:
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 .