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Learning to Generate Visual Questions with Noisy Supervision

NeurIPS 2021 · Vision-language and multimodal › Visual question answering · approach: Multimodal question answering datasets · 4 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Remove visual hints (ablation)DH-GAN generator · VQA 2.0 valB@4: 22.60 -> 21.87Hurt
Replace answer hints with only answer type (also remove visual hints) (ablation)DH-GAN generator · VQA 2.0 valB@4: 21.87 -> 18.63Hurt
Add GAN discriminator on top of the pretrained generator (ablation)DH-GAN · VQA 2.0 valB@4: 22.60 -> 23.71Helped
Remove visual hints sampling module (ablation)DH-GAN · VQA 2.0 valB@4: 23.71 -> 23.53Hurt
Remove visual hints sampling module (ablation)DH-GAN · VQA 2.0 valMETEOR: 22.91 -> 22.87Hurt
Add GAN discriminator (DH-GAN vs generator only) in human evaluation (ablation)DH-GAN · VQA2.0 human studyrelevance: 3.86 -> 4Helped
Replace Radial with DH-GAN for question generationDH-GAN · VQA 2.0 valCIDEr: 162.92 -> 191.06Helped
Replace DH-VQG with DH-GAN (GAN noisy-hint refinement)DH-GAN · COCO-QA valCIDEr: 175.18 -> 186.65Helped
Replace DH-VQG with DH-GANDH-GAN · VQA 2.0 valB@4: 22.43 -> 23.71Helped
Augment BUTD/BAN training with Radial-generated questionsVQA models · VQA2.0 data augmentationclassification accuracyHurt
Augment VQA training with DH-GAN-generated questionsBUTD and BAN · VQA2.0 data augmentation (30/50/70% data)classification accuracyHelped
Add VQG-generated questions to train zero-shot VQA (Radial)BUTD · ZS-VQA VQA2.0accuracy: 0% -> 18.15%Helped

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