An Intention-Aware Interactive System for Mobile Video Browsing

Jun He, Hanwang Zhang, Ling Shen, Richang Hong, Tat-Seng Chua

Abstract: Video browsing describes an interactive process where the user wants to find a target shot in a long video. Therefore, it is crucial for a video browsing system to be fast and accurate with minimum user effort. Previously, video browsing systems suffered from either lots of user efforts or heavy computation cost, which makes them less effective. We propose an RNN-based \textit{intention-aware} system to overcome these limitations. At each interactive round, the user only needs to select one of the displayed shots that is most visually similar to her mental target and then the user's choice will further tailor the search to the target. The search model update merely requires vector inner products which means the system is highly responsive.