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Research map / Vision-language and multimodal / Human and embodied modeling

Multi-Modal Representation Learning With Text-Driven Soft Masks

CVPR 2023 · Vision-language and multimodal › Human and embodied modeling · approach: Language-grounded scene text and structure · 4 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add text-driven SoftMask (ablation)SoftMask++ pretraining · MS-COCO fine-tuned image-text retrievalTR@1: 73.10 -> 75.74Helped
add focal ITC loss (ablation)SoftMask++ pretraining · MS-COCO fine-tuned image-text retrievalTR@1: 73.10 -> 75.66Helped
add multimodal data augmentation (ablation)SoftMask++ pretraining · MS-COCO fine-tuned image-text retrievalTR@1: 73.10 -> 75.26Helped
replace text-driven soft masks with random hard masks (p=0.3) (ablation)SoftMask++ · MS-COCO fine-tuned retrievalTR@1: 76.62 -> 75.82Hurt
replace text-driven soft masks with random hard masks (p=0.5) (ablation)SoftMask++ · MS-COCO fine-tuned retrievalTR@1: 76.62 -> 76.22Hurt
also apply SoftMask to MLM (ablation)SoftMask++ with SoftMask on ITM · MS-COCO fine-tuned retrievalTR@1: 76.62 -> 76.21Hurt
use normalized cross-attention instead of Grad-CAM to generate masks (ablation)SoftMask++ · MS-COCO fine-tuned retrievalTR@1: 76.62 -> 75.68Hurt
combine SoftMask, focal ITC, and MMDA (ablation)SoftMask++ pretraining · MS-COCO fine-tuned retrievalTR@1: 73.10 -> 76.62Helped
pretrain with SoftMask++ rather than TCLvision-language representation learning · Flickr30K fine-tuned text retrievalR@1: 94.9 -> 95.4Helped
pretrain with SoftMask++ rather than TCLvision-language representation learning · MS-COCO zero-shot image retrievalR@1: 53.5 -> 54.1Helped

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