Visual complexity is not captured by segmentation counts alone; multi-scale gradients, color diversity, and LLM-generated surprise scores improve prediction on several datasets, including a new Surprising Visual Genome set.
AMNet: Memorability Estimation with Attention
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abstract
In this paper we present the design and evaluation of an end-to-end trainable, deep neural network with a visual attention mechanism for memorability estimation in still images. We analyze the suitability of transfer learning of deep models from image classification to the memorability task. Further on we study the impact of the attention mechanism on the memorability estimation and evaluate our network on the SUN Memorability and the LaMem datasets. Our network outperforms the existing state of the art models on both datasets in terms of the Spearman's rank correlation as well as the mean squared error, closely matching human consistency.
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Complexity in Complexity: Understanding Visual Complexity Through Structure, Color, and Surprise
Visual complexity is not captured by segmentation counts alone; multi-scale gradients, color diversity, and LLM-generated surprise scores improve prediction on several datasets, including a new Surprising Visual Genome set.