Learnable Diffusion Timesteps for Few-shot Dense Prediction

This paper addresses the challenge of selecting optimal diffusion timesteps in diffusion models for few-shot dense prediction tasks. It proposes two modules, Task-aware Timestep Selection (TTS) and Timestep Feature Consolidation (TFC), to adaptively choose and consolidate timestep features, improving performance in few-shot scenarios. The work focuses on universal and few-shot learning, making it relevant for practical applications.

• •Addresses the problem of suboptimal diffusion timestep selection in diffusion models.
• •Proposes TTS and TFC modules for adaptive timestep selection and consolidation.
• •Focuses on few-shot dense prediction, making it applicable to practical scenarios.
• •Evaluated on the Taskonomy dataset.