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ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

NeurIPS 2019 · Vision-language and multimodal › Visual question answering · approach: Vision-language model VQA evaluation · 1,665 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add co-attention and correlation pretraining rather than using masking loss only with zeroed co-attention (ablation)ViLBERT · VQAscore: 64.85 -> 66.59Helped
Add co-attention and correlation pretraining rather than using masking loss only with zeroed co-attention (ablation)ViLBERT · RefCOCO+score: 68.04 -> 70.38Helped
Add alignment loss (ablation)ViLBERT · VQAscore: 64.61 -> 66.59Helped
Add alignment loss (ablation)ViLBERT · RefCOCO+score: 68.49 -> 70.38Helped
Add masking loss (ablation)ViLBERT · VQAscore: 42.43 -> 66.59Helped
Add masking loss (ablation)ViLBERT · RefCOCO+score: 10.00 -> 70.38Helped

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