# Image Memorability

After progressive Gaussian noise was added to LaMem images, mainstream memorability models such as ResMem, ViTMem and AMNet estimated lower memorability for initially memorable images and higher memorability for initially forgettable ones, a reversal resembling stochastic resonance. In a continuous recognition task with 88 participants, memorable images proved more resistant to noise while forgettable images declined sharply. The authors built GraphMem, which estimates memorability from an image's neighborhood in a similarity graph rather than from single-image features; it aligned most closely with human data under noise and blur, and after noise training its slope effect reached ρ=0.57 (95% bootstrap CI [0.29, 0.75]), the only model to turn positive.

After progressive Gaussian noise was added to LaMem images, mainstream memorability models such as ResMem, ViTMem and AMNet estimated lower memorability for initially memorable images and higher memorability for initially forgettable ones, a reversal resembling stochastic resonance. In a continuous recognition task with 88 participants, memorable images proved more resistant to noise while forgettable images declined sharply. The authors built GraphMem, which estimates memorability from an image's neighborhood in a similarity graph rather than from single-image features; it aligned most closely with human data under noise and blur, and after noise training its slope effect reached ρ=0.57 (95% bootstrap CI [0.29, 0.75]), the only model to turn positive.