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Research map / Vision-language and multimodal / Visual question answering

Maintaining Reasoning Consistency in Compositional Visual Question Answering

CVPR 2022 · Vision-language and multimodal › Visual question answering · approach: Multimodal question answering datasets · 29 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Integrate sub-question answering into the compositional-question reasoning process (ablation)Graph reasoning model on GQA/GQA-Sub · GQA-Sub validation-subRC(3): 51.62 -> 52.80Helped
Add the consistency constraint (ablation)Dialog-like reasoning model · GQA-Sub validation-subRC(3): 51.64 -> 52.80Helped
Use the ordered sub-question iteration schedule instead of random iteration counts (ablation)Dialog-like reasoning model · GQA-Sub validation-subRC(3): 50.25 -> 52.80Helped
Add data augmentation using GQA-Sub sub-questions (ablation)Graph reasoning model · GQA validationaccuracy: 65.29 -> 65.58No clear effect
Add data augmentation using GQA-Sub sub-questions (ablation)Graph reasoning model · GQA-Sub validation-subRC(3): 33.62 -> 51.62Helped
Use dialog-like reasoning and the consistency constraint rather than the strongest reported augmented baselineGQA-Sub validation-sub · GQA-Sub validation-subRC(3): 50.05 -> 52.80Helped
Use the proposed method rather than MDETRGQA test · GQA testaccuracy: 61.99 -> 59.58Hurt

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