Technical Report for CVPR 2022 LOVEU AQTC Challenge
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Abstract (arXiv)
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.
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References · 9
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
- 2015VQA: Visual Question Answeringcited by 2,506
- 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
- 2016Visual Dialogcited by 377
- 2017Embodied Question Answeringcited by 336
- 2019Audio-Visual Scene-Aware Dialogcited by 123
- 2019Multi-Target Embodied Question Answeringcited by 74
- 2022AssistQ: Affordance-centric Question-driven Task Completion for Egocentric Assistantcited by 32
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