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UTC: A Unified Transformer With Inter-Task Contrastive Learning for Visual Dialog

CVPR 2022 · Vision-language and multimodal › Human and embodied modeling · approach: Vision-language pretraining · 38 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use UTC rather than VDBERTVisDial v1.0 val discriminative · VisDial v1.0 valR@1: 54.02 -> 55.48Helped
Use UTC rather than LTMIVisDial v1.0 val generative · VisDial v1.0 valR@1: 40.44 -> 42.56Helped
Use UTC rather than LTMIVisDial v1.0 val generative · VisDial v1.0 valR@10: 69.71 -> 69.51Hurt
Apply UTC contrastive learning to the VisDial-BERT fine-tuned modelVisDial v1.0 test discriminative · VisDial v1.0 testNDCG: 63.87 -> 64.60Helped
Add joint training of the generative task to the ranking task (ablation)UTC discriminative · VisDial v1.0 valR@1: 53.94 -> 54.39Helped
Add joint training of the discriminative task to the generation task (ablation)UTC generative · VisDial v1.0 valR@1: 41.39 -> 41.75Helped
Add answer and context contrastive losses (ablation)UTC discriminative · VisDial v1.0 valR@1: 54.39 -> 55.48Helped
Add answer and context contrastive losses (ablation)UTC generative · VisDial v1.0 valR@1: 41.75 -> 42.56Helped
Add answer contrastive loss (ablation)UTC generative · VisDial v1.0 valR@1: 41.75 -> 42.56Helped
Add context contrastive loss (ablation)UTC discriminative · VisDial v1.0 valR@1: 54.39 -> 55.24Helped
Add context contrastive loss to answer-contrastive training (ablation)UTC generative · VisDial v1.0 valR@1: 42.52 -> 42.56No clear effect
Add answer contrastive loss to context-contrastive training (ablation)UTC discriminative · VisDial v1.0 valR@1: 55.24 -> 55.48No clear effect

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