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

Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts

NeurIPS 2017 · Vision-language and multimodal › Human and embodied modeling · approach: Visual grounding transformers · 12 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the full Prior + Geo + Seg + Det model instead of CCATextual grounding · Flickr 30k Entitiesaccuracy: 50.89 -> 51.63Helped
Add detection maps plus body-part detection to Prior + Geo + Seg + Det (ablation)Textual grounding · Flickr 30k Entitiesaccuracy: 51.63 -> 53.97Helped
Add segmentation maps to word priors and geometric features (ablation)Textual grounding · ReferItGameaccuracy: 25.56 -> 33.36Helped
Add detection maps to Prior + Geo + Seg (ablation)Textual grounding · ReferItGameaccuracy: 33.36 -> 34.70Helped
Use the full model instead of GroundeRTextual grounding · ReferItGameaccuracy: 23.44 -> 34.70Helped
Use the full model instead of CCATextual grounding · Flickr 30k Entities: peopleaccuracy: 64.73 -> 68.71Helped
Use the full model instead of CCATextual grounding · Flickr 30k Entities: clothingaccuracy: 46.88 -> 46.83Hurt
Use the full model instead of CCATextual grounding · Flickr 30k Entities: body partsaccuracy: 17.21 -> 19.50Helped
Use the full model instead of CCATextual grounding · Flickr 30k Entities: animalsaccuracy: 65.83 -> 70.07Helped
Use the full model instead of CCATextual grounding · Flickr 30k Entities: vehiclesaccuracy: 68.75 -> 73.75Helped
Use the full model instead of CCATextual grounding · Flickr 30k Entities: instrumentsaccuracy: 37.65 -> 39.50Helped
Use the full model instead of CCATextual grounding · Flickr 30k Entities: sceneaccuracy: 51.39 -> 60.38Helped

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