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Event-Specific Image Importance

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

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use the proposed progressive piecewise-ranking CNN instead of the strongest non-oracle baselineevent-specific image importance prediction · CUFED test setMAP@5%: 0.266 -> 0.302Helped
add the initial shared-output training stage before event-specific training (ablation)PR-CNN · CUFED test setMAP@5%: 0.296 -> 0.302Helped
include event-type information in the image importance prediction (ablation)PR-CNN · CUFED test setMAP@5%: 0.261 -> 0.302Helped
use piecewise ranking loss rather than conventional ranking SVM hinge loss (ablation)CNN image-importance ranker · CUFED test setMAP@5%: 0.266 -> 0.296Helped
add face heatmap-based scores (ablation)Ensemble-CNN · Beach TripMAP@5%: +0.051Helped
add face heatmap-based scores (ablation)Ensemble-CNN · Nature TripMAP@5%: +0.008Helped
add face heatmap-based scores (ablation)Ensemble-CNN · Group ActivityMAP@5%: +0.003Helped
add face heatmap-based scores (ablation)Ensemble-CNN · HalloweenMAP@5%: +0.000No clear effect
add face heatmap-based scores (ablation)Ensemble-CNN · MuseumMAP@5%: -0.010Hurt
add face heatmap-based scores (ablation)Ensemble-CNN · CUFED test set across event typesMAP: about 0.1%No clear effect
use aesthetics scores instead of event-specific importance predictionimage ranking for event albums · CUFED test setMAP@5%: 0.122 -> 0.139Hurt

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