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Reasoning with Heterogeneous Graph Alignment for Video Question Answering

AAAI 2020 · Vision-language and multimodal › Visual question answering · approach: Multimodal question answering datasets · 188 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
HGA vs ST-VQA*VideoQA · TGIF-QA Actionaccuracy: 73.5 -> 75.4Helped
HGA vs PSACVideoQA · TGIF-QA FrameQAaccuracy: 55.7 -> 55.1Hurt
HGA vs Fan et al.VideoQA · MSRVTT-QAaccuracy: 33.0 -> 35.5Helped
add CoAttn and GCN to baseline (ablation)parallel bilinear baseline · TGIF-QA Trans.accuracy: 78.1 -> 81.0Helped
add GCN reasoning only (ablation)baseline · TGIF-QA FrameQAaccuracy: 55.7 -> 53.9Hurt
add GCN reasoning only (ablation)baseline · TGIF-QA Actionaccuracy: 73.4 -> 74.5Helped
add co-attention embedding only (ablation)baseline · TGIF-QA Actionaccuracy: 73.4 -> 73.9Helped
add global fusion branch (ablation)HGA · TGIF-QA Actionaccuracy: 73.6 -> 75.4Helped
add local fusion branch (ablation)HGA · TGIF-QA Actionaccuracy: 71.3 -> 75.4Helped
add global fusion to GRU local-fusion model (ablation)GRU single-flow · TGIF-QA Actionaccuracy: 55.4 -> 73.4Helped
Siamese/PseudoSiamese CAEO variants instead of CoAttn (ablation)HGA · TGIF-QAaccuracyHurt

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