Understanding the multi-scale visual information
in a video is essential for Video Question Answering (VideoQA).
Therefore, we propose a novel Multi-Scale Progressive Attention Network 
(MSPAN)
to achieve relational reasoning between cross-scale video information.
We construct clips of different lengths to represent different 
scales of the video.
Then, the clip-level features are aggregated into node features 
by using max-pool, 
and a graph is generated for each scale of clips.
For cross-scale feature interaction, 
we design a message passing strategy between adjacent scale graphs, 
i.e., top-down scale interaction and bottom-up scale interaction.
Under the question's guidance of progressive attention, 
we realize the fusion of all-scale video features.
Experimental evaluations on three
benchmarks: TGIF-QA, MSVD-QA and MSRVTT-QA show
our method has achieved state-of-the-art performance.

Multi-Scale Progressive Attention Network for Video Question Answering

Poster 3Q: Question Answering

poster

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IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

Zhicheng Guo

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Multi-Scale Progressive Attention Network for Video Question Answering

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