Correlates of Image Memorability in Vision Encoders: Activations, Attention Entropy, Patch Uniformity and Autoencoder Losses

ArXi:2509.01453v2 Announce Type: replace Images vary in how memorable they are to humans. Inspired by findings from cognitive science and computer vision, we explore correlates of image memorability in pretrained transformer-based vision encoders for the first time. Focusing initially on activations, attention distributions, and the uniformity of image patches, we find that these features correlate with memorability to some extent.