PDPP: Projected Diffusion for Procedure Planning in Instructional Videos.

Area of Science:

• Computer Vision
• Machine Learning
• Robotics

Background:

• Procedure planning in instructional videos is crucial for tasks like robotics and human assistance.
• Existing methods often rely on complex autoregressive modeling with expensive intermediate supervision or language instructions.
• These methods can suffer from error accumulation and high annotation costs.

Purpose of the Study:

• To develop a novel diffusion-based framework for procedure planning that avoids intermediate supervision and autoregressive errors.
• To directly model the distribution of action sequences using only task labels and observations.
• To improve the efficiency and reduce the annotation burden of procedure planning.

Main Methods:

• Proposed PDPP (Projected Diffusion model for Procedure Planning), a diffusion-based framework for direct action sequence modeling.
• Treated procedure planning as a distribution fitting problem, transforming it into a sampling process.
• Utilized joint training for variable horizon length plans and explored condition-introducing methods like embeddings, MoEs, and Classifier-Free Guidance.
• Applied the framework to the Visual Planners for Human Assistance (VPA) problem with natural language goals.

Main Results:

• Achieved state-of-the-art performance on multiple metrics across challenging datasets.
• Demonstrated effectiveness and generalization ability compared to strongly-supervised methods.
• Successfully applied to the VPA problem, handling natural language goal specifications.

Conclusions:

• The proposed PDPP framework offers an effective and efficient approach to procedure planning.
• Diffusion-based modeling successfully addresses uncertainty and reduces reliance on costly intermediate supervision.
• PDPP shows strong performance and generalization, paving the way for advanced instructional video understanding and robotic applications.