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Diffusing Surrogate Dreams of Video Scenes to Predict Video Memorability
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As part of the MediaEval 2022 Predicting Video Memorability task we explore the relationship between visual memorability, the visual representation that characterises it, and the underlying concept portrayed by that visual representation. We achieve state-of-the-art memorability prediction performance with a model trained and tested exclusively on surrogate dream images, elevating concepts to the status of a cornerstone memorability feature, and finding strong evidence to suggest that the intrinsic memorability of visual content can be distilled to its underlying concept or meaning irrespective of its specific visual representational.
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Cited by 2 Pith papers
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Enhancing Video Memorability Prediction with Text-Motion Cross-modal Contrastive Loss and Its Application in Video Summarization
The paper proposes a text-motion contrastive loss (TMCCL) that improves video memorability prediction and a memorability-weighted correction for video summarization, but the loss as written has a sign error.
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