When browsing large video collections, human-in-the-loop systems are essential. The system should understand the semantic information need of the user and interactively help formulate queries to satisfy that information need based on data-driven methods. Full synergy between the interacting user and the system can only be obtained when the system learns from the user interactions while providing immediate response. Doing so with dynamically changing information needs for large scale multimodal collections is a challenging task. To push the boundary of current methods, we propose to apply the state of the art in interactive multimodal learning to the complex multimodal information needs posed by the Video Browser Showdown (VBS). To that end we adapt the Exquisitor system, a highly scalable interactive learning system. Exquisitor combines semantic features extracted from visual content and text to suggest relevant media items to the user, based on user relevance feedback on previously suggested items. In this paper, we briefly describe the Exquisitor system, and its first incarnation as a VBS entrant.
|Title of host publication||Proceedings of the International Conference on MultiMedia Modeling (MMM)|
|Editors||Yong Man Ro, Wen-Huang Cheng, Junmo Kim, Wei-Ta Chu, Peng Cui, Jung-Woo Choi, Min-Chun Hu, Wesley De Neve|
|Place of Publication||Daejeon, Korea|
|Publication date||Jan 2020|
|Publication status||Published - Jan 2020|
- Interactive learning
- Video browsing