Toward Accurate Procedure Planning in Instructional Videos: Visual State Generation Helps Task-Selective Diffusion.

Area of Science:

• Computer Science
• Robotics
• Artificial Intelligence

Background:

• Procedure planning in instructional videos is complex due to limited visual data and vast action possibilities.
• Existing methods often implicitly handle uncertainty, leading to suboptimal performance.

Purpose of the Study:

• To develop an explicit solution for procedure planning that tackles uncertainty in visual observations and decision spaces.
• To improve the accuracy and efficiency of predicting action sequences for instructional videos.

Main Methods:

• Utilized image generation models and a prompt selection module within a diffusion model to synthesize diverse intermediate visual states.
• Introduced a task-selective diffusion model with a task-specific mask to constrain the action space.
• Enhanced visual representation using pre-trained vision-language models for action-aware, text-enriched multimodal embeddings.

Main Results:

• The proposed approach demonstrated superior performance on benchmark datasets compared to prior methods.
• The combination of synthesized states and a constrained action space significantly improved procedure planning accuracy.
• Action-aware multimodal embeddings enhanced task classification and subsequent action prediction.

Conclusions:

• The developed method effectively mitigates uncertainty in visual observations and decision spaces for procedure planning.
• This explicit approach offers a significant advancement in generating accurate and contextually relevant action sequences for instructional videos.
• The findings have implications for robotics, AI-driven instruction, and automated task execution.