Toward a Diffusion-Based Generalist for Dense Vision Tasks

• URL: http://arxiv.org/abs/2407.00503v1
• Date: Sat, 29 Jun 2024 17:57:22 GMT
• Title: Toward a Diffusion-Based Generalist for Dense Vision Tasks
• Authors: Yue Fan, Yongqin Xian, Xiaohua Zhai, Alexander Kolesnikov, Muhammad Ferjad Naeem, Bernt Schiele, Federico Tombari,
• Abstract summary: Recent works have shown image itself can be used as a natural interface for general-purpose visual perception. We propose to perform diffusion in pixel space and provide a recipe for finetuning pre-trained text-to-image diffusion models for dense vision tasks. In experiments, we evaluate our method on four different types of tasks and show competitive performance to the other vision generalists.
• Score: 141.03236279493686
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Building generalized models that can solve many computer vision tasks simultaneously is an intriguing direction. Recent works have shown image itself can be used as a natural interface for general-purpose visual perception and demonstrated inspiring results. In this paper, we explore diffusion-based vision generalists, where we unify different types of dense prediction tasks as conditional image generation and re-purpose pre-trained diffusion models for it. However, directly applying off-the-shelf latent diffusion models leads to a quantization issue. Thus, we propose to perform diffusion in pixel space and provide a recipe for finetuning pre-trained text-to-image diffusion models for dense vision tasks. In experiments, we evaluate our method on four different types of tasks and show competitive performance to the other vision generalists.

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