Scaling Properties of Diffusion Models for Perceptual Tasks
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 segmentation
under image-to-image translation, and show how diffusion models benefit from
scaling training and test-time compute for these perception tasks. Through a
careful analysis of these scaling behaviors, we present various techniques to
efficiently train diffusion models for visual perception tasks. Our models
achieve improved or comparable performance to state-of-the-art methods using
significantly less data and compute. To use our code and models, see
https://scaling-diffusion-perception.github.io .
67341e4ffce00b60eb491c72
Rahul Ravishankar
67341e4ffce00b60eb491c73
Zeeshan Patel
admin_assigned
2024-11-18T08:08:35.621Z
638ee4df6251c8bd7abd246e
/avatars/8d8c4dac7d82cba6dcf884460bdc5328.svg
Zeeshan Patel
user
zeeshanp
67341e4ffce00b60eb491c74
Jathushan Rajasegaran
admin_assigned
2024-11-18T08:08:40.499Z
63895b3a43d8b0797a0d0406
/avatars/04d9952dff70f85d4e481ec80ae818cb.svg
Jathushan Rajasegaran
user
brjathu
67341e4ffce00b60eb491c75
Jitendra Malik
admin_assigned
2024-11-18T08:08:45.996Z
65369a95605a07338de78ab0
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/sGFjOjLT2akN-sn5beVWL.jpeg
Jitendra Malik 
user
jitendra1995
Scaling Properties of Diffusion Models for Perceptual Tasks
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 segmentation
under image-to-image translation, and show how diffusion models benefit from
scaling training and test-time compute for these perception tasks. Through a
careful analysis of these scaling behaviors, we present various techniques to
efficiently train diffusion models for visual perception tasks. Our models
achieve improved or comparable performance to state-of-the-art methods using
significantly less data and compute. To use our code and models, see
https://scaling-diffusion-perception.github.io .
Diffusion models enhance visual perception tasks like depth estimation, optical flow, and segmentation through efficient training and reduced data and compute requirements.
iterative computation diffusion models image-to-image translation scaling behaviors efficient training visual perception tasks