mlsanityData to 14 Oct 2026

Diffusing Surrogate Dreams of Video Scenes to Predict Video Memorability

arXiv 2212.09308 ↗First posted 19 Dec 2022cs.CV cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 7 of its 17 references that are in the map. First posted on arXiv 19 Dec 2022.

Builds on

7 of 17 references.

References · 7

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,152
  4. 2020Multimodal Memorability: Modeling Effects of Semantics and Decay on Video Memorabilitycited by 23
  5. 2020Leveraging Audio Gestalt to Predict Media Memorabilitycited by 5
  6. 2021The Influence of Audio on Video Memorability with an Audio Gestalt Regulated Video Memorability Systemcited by 5
  7. 2022Overview of The MediaEval 2022 Predicting Video Memorability Taskcited by 5

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Citing papers per year: 2023: 2; 2024: 0; 2025: 2.

4 papers.