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SemStyle: Learning to Generate Stylised Image Captions Using Unaligned Text

CVPR 2018 · Vision-language and multimodal › Human and embodied modeling · approach: Concept-guided image captioning · 131 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Separate image semantics into terms and generate the target style from those termsSemStyle · Romance-styled MSCOCO captionsSPICE: 0.144 vs 0.088Helped
Use the target-style term to generate romance-style captionsSemStyle · MSCOCO human style evaluationstory-like captions: 41.9% vs 6.2%Helped
Randomize semantic term ordering (ablation)SemStyle · Romance-styled MSCOCO captionsSPICE: 0.144 -> 0.134Hurt
Randomize semantic term ordering (ablation)SemStyle · Romance-styled MSCOCO captionsCLF: 0.589 -> 0.501Hurt
Use raw words instead of POS-tagged, lemmatized, FrameNet terms (ablation)SemStyle · Romance-styled MSCOCO captionsCLF: 0.589 -> 0.407Hurt
Use verb lemmas instead of FrameNet frames (ablation)SemStyle · Romance-styled MSCOCO captionsCLF: 0.589 -> 0.533Hurt
Use verb lemmas instead of FrameNet frames (ablation)SemStyle · Romance-styled MSCOCO captionsSPICE: 0.144 -> 0.148No clear effect
Train the language generator on romance text only rather than both romance and descriptive captions (ablation)SemStyle · Romance-styled MSCOCO captionsSPICE: 0.144 -> 0.138Hurt
Train the language generator on romance text only rather than both romance and descriptive captions (ablation)SemStyle · MSCOCO human relevance evaluationclear and accurate captions: 43.8% -> 34.7%Hurt
Use ground-truth ordered terms instead of randomized term order (ablation)SemStyle term generator · MSCOCOSPICE: 0.134 -> 0.144Helped
Use a descriptive target-style term at test time (ablation)SemStyle · MSCOCOSPICE: 0.154 -> 0.157No clear effect
Use scheduled sampling instead of teacher forcing during term-generator training (ablation)SemStyle term generator · MSCOCOterm-generator performanceNo clear effect

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