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Predicting Event Memorability from Contextual Visual Semantics
Qianli Xu · Fen Fang · Ana Molino · Vigneshwaran Subbaraju · Joo-Hwee Lim

Tue Dec 07 04:30 PM -- 06:00 PM (PST) @ Virtual #None

Episodic event memory is a key component of human cognition. Predicting event memorability,i.e., to what extent an event is recalled, is a tough challenge in memory research and has profound implications for artificial intelligence. In this study, we investigate factors that affect event memorability according to a cued recall process. Specifically, we explore whether event memorability is contingent on the event context, as well as the intrinsic visual attributes of image cues. We design a novel experiment protocol and conduct a large-scale experiment with 47 elder subjects over 3 months. Subjects’ memory of life events is tested in a cued recall process. Using advanced visual analytics methods, we build a first-of-its-kind event memorability dataset (called R3) with rich information about event context and visual semantic features. Furthermore, we propose a contextual event memory network (CEMNet) that tackles multi-modal input to predict item-wise event memorability, which outperforms competitive benchmarks. The findings inform deeper understanding of episodic event memory, and open up a new avenue for prediction of human episodic memory. Source code is available at https://github.com/ffzzy840304/Predicting-Event-Memorability.

Author Information

Qianli Xu (Institute for Infocomm Research)
Fen Fang (Institute for Infocomm Research)
Ana Molino (Nanyang Technological University)
Vigneshwaran Subbaraju (Institute of High Performance Computing)
Joo-Hwee Lim (Institute for Infocomm Research, Singapore)

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