  This technical report presents the 2nd winning model for AQTC, a task newly
introduced in CVPR 2022 LOng-form VidEo Understanding (LOVEU) challenges. This
challenge faces difficulties with multi-step answers, multi-modal, and diverse
and changing button representations in video. We address this problem by
proposing a new context ground module attention mechanism for more effective
feature mapping. In addition, we also perform the analysis over the number of
buttons and ablation study of different step networks and video features. As a
result, we achieved the overall 2nd place in LOVEU competition track 3,
specifically the 1st place in two out of four evaluation metrics. Our code is
available at https://github.com/jaykim9870/ CVPR-22_LOVEU_unipyler.
