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

Predicting Motivations of Actions by Leveraging Text

CVPR 2016 · Vision-language and multimodal › Human and embodied modeling · approach: Human perception modeling · 40 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add text-mined language potentials to visual classifiersVision model · Motivations dataset testmedian rank (motivation): 39 -> 28Helped
Add the person crop featuresVision model · Motivations dataset testmedian rank (motivation): 39 -> 42Hurt
Add person crop features to the text-augmented modelVision + Text · Motivations dataset testmedian rank (motivation): 28 -> 27Helped
Use a web-trained language model instead of the Wikipedia language modelVision + Person + Text · Motivations dataset testmedian rank (motivation): 28 -> 27Helped
Provide the ground-truth action to the model (ablation)Vision + Person + Text · Motivations dataset testmedian rank (motivation): 27 -> 26Helped
Provide the ground-truth scene to the model (ablation)Vision + Person + Text · Motivations dataset testmedian rank (motivation): 27 -> 27No clear effect
Provide both the ground-truth action and scene to the model (ablation)Vision + Person + Text · Motivations dataset testmedian rank (motivation): 27 -> 25Helped
Add text-mined language potentials to visual classifiersVision model · Motivations dataset testmedian rank (action): 17 -> 15Helped
Add text-mined language potentials to visual classifiersVision model · Motivations dataset testmedian rank (scene): 4 -> 3Helped

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