[2206.14555] Technical Report for CVPR 2022 LOVEU AQTC Challenge
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Computer Science > Computer Vision and Pattern Recognition

arXiv:2206.14555 (cs)
[Submitted on 29 Jun 2022]

Title:Technical Report for CVPR 2022 LOVEU AQTC Challenge

Authors:Hyeonyu Kim, Jongeun Kim, Jeonghun Kang, Sanguk Park, Dongchan Park, Taehwan Kim
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Abstract: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 this https URL CVPR-22_LOVEU_unipyler.
Comments: 4 pages, 3 figures, technical report for track3 of CVPR 2022 LOVEU challenge
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2206.14555 [cs.CV]
  (or arXiv:2206.14555v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2206.14555

Submission history

From: Hyeonyu Kim [view email]
[v1] Wed, 29 Jun 2022 12:07:43 UTC (1,102 KB)
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