Eye vs. AI: Human Gaze and Model Attention in Video Memorability

Understanding the factors that determine video memorability has important applications in areas such as educational technology and advertising. Towards this goal, we investigate the semantic and temporal attention mechanisms underlying video memorability. We propose a Transformer-based model with spatio-temporal attention that matches SoTA performance on video memorability prediction on a large naturalistic video dataset. More importantly, the self-attention patterns show us where the model looks to predict memorability. We compare model attention against human gaze fixation density maps collected through a small-scale eye-tracking experiment where humans perform a video memory task. Quantitative saliency metrics show that the model attention and human gaze follow similar patterns. Furthermore, while panoptic segmentation confirms that the model and humans attend more to thing classes, stuff classes that receive increased/decreased attention tend to have higher memorability scores. We also observe that the model assigns greater importance to the initial frames, mimicking temporal attention patterns found in humans.