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Research map / Detection and segmentation / Open-set and few-shot detection

Partially-Supervised Image Captioning

NeurIPS 2018 · Detection and segmentation › Open-set and few-shot detection · approach: Object-level representation learning · 26 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Train with image labels using PS3 rather than captions aloneUp-Down · COCO novel object validation, out-of-domainCIDEr: 69.5 -> 94.3Helped
Use PS3 training instead of relying on constrained decoding with predicted labelsUp-Down · COCO novel object validation, out-of-domainCIDEr: 74.8 -> 94.3Helped
Add constrained beam search decoding after PS3 training (ablation)Up-Down with PS3 · COCO novel object validation, out-of-domainCIDEr: 94.3 -> 92.5Hurt
Train with image labels using PS3Up-Down · COCO novel object validation, in-domainCIDEr: 108.6 -> 101.2Hurt
Train jointly with labels for 25 Open Images animal classesUp-Down · COCO Karpathy valCIDEr: 112.3 -> 103.5Hurt
Add fixed word embeddings without constrained decoding (ablation)Up-Down · COCO novel object validation, out-of-domainCIDEr: 69.5 -> 66.4Hurt
Use both GloVe and dependency embeddings rather than GloVe alone with T2of3 decoding (ablation)Up-Down · COCO novel object validation, out-of-domainCIDEr: 80.2 -> 84.1Helped
Use both GloVe and dependency embeddings rather than GloVe alone with T3of3 decoding (ablation)Up-Down · COCO novel object validation, out-of-domainCIDEr: 80.1 -> 82.2Helped
Select three labels and require at least two to be mentioned, rather than the tested alternatives (ablation)PS3 image-caption training · COCO novel object captioningcaptioning scoresHelped

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