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arXiv · 2311.16484

Seeing Eye to AI: Comparing Human Gaze and Model Attention in Video Memorability

Prajneya Kumar·Eshika Khandelwal·Makarand Tapaswi·Vishnu Sreekumar
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Abstract

Understanding what makes a video memorable has important applications in advertising or education technology. Towards this goal, we investigate spatio-temporal attention mechanisms underlying video memorability. Different from previous works that fuse multiple features, we adopt a simple CNN+Transformer architecture that enables analysis of spatio-temporal attention while matching state-of-the-art (SoTA) performance on video memorability prediction. We compare model attention against human gaze fixations collected through a small-scale eye-tracking study where humans perform the video memory task. We uncover the following insights: (i) Quantitative saliency metrics show that our model, trained only to predict a memorability score, exhibits similar spatial attention patterns to human gaze, especially for more memorable videos. (ii) The model assigns greater importance to initial frames in a video, mimicking human attention patterns. (iii) Panoptic segmentation reveals that both (model and humans) assign a greater share of attention to things and less attention to stuff as compared to their occurrence probability.

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Prajneya Kumar, Eshika Khandelwal, Makarand Tapaswi, Vishnu Sreekumar. 2023-11-26. Seeing Eye to AI: Comparing Human Gaze and Model Attention in Video Memorability. https://arxiv.org/abs/2311.16484

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