  Affordance-Centric Question-driven Task Completion (AQTC) has been proposed
to acquire knowledge from videos to furnish users with comprehensive and
systematic instructions. However, existing methods have hitherto neglected the
necessity of aligning spatiotemporal visual and linguistic signals, as well as
the crucial interactional information between humans and objects. To tackle
these limitations, we propose to combine large-scale pre-trained
vision-language and video-language models, which serve to contribute stable and
reliable multimodal data and facilitate effective spatiotemporal visual-textual
alignment. Additionally, a novel hand-object-interaction (HOI) aggregation
module is proposed which aids in capturing human-object interaction
information, thereby further augmenting the capacity to understand the
presented scenario. Our method achieved first place in the CVPR'2023 AQTC
Challenge, with a Recall@1 score of 78.7\%. The code is available at
https://github.com/tomchen-ctj/CVPR23-LOVEU-AQTC.
