Winning the CVPR'2022 AQTC Challenge: A Two-stage Function-centric Approach
This work addresses the problem of AI assistants learning from instructional videos for step-by-step guidance, representing an incremental improvement in a domain-specific task.
The paper tackled the AQTC challenge by proposing a two-stage function-centric approach, achieving significant gains over baselines in grounding questions to functions and predicting actions from historical steps.
Affordance-centric Question-driven Task Completion for Egocentric Assistant(AQTC) is a novel task which helps AI assistant learn from instructional videos and scripts and guide the user step-by-step. In this paper, we deal with the AQTC via a two-stage Function-centric approach, which consists of Question2Function Module to ground the question with the related function and Function2Answer Module to predict the action based on the historical steps. We evaluated several possible solutions in each module and obtained significant gains compared to the given baselines. Our code is available at \url{https://github.com/starsholic/LOVEU-CVPR22-AQTC}.