Abstract
Micro-videos, which contain interesting events occurring at a specific venue, are uploaded to social platforms with a high degree of subjectivity and arbitrariness. Because the duration of a micro-video is shorter than six seconds, the scene in the micro-video is always from a single venue. Therefore, the venue is an important location-related piece of information in micro-videos. In this preliminary research, we investigate micro-video venue retrieval. First we present the challenges of micro-video venue retrieval and then propose a new strategy based on a multi-layer neural network. Finally, we evaluate the proposed strategy on an actual micro-video dataset crawled from Vine. The experimental results show its superior performance.
This work is supported by the National Natural Science Foundation of China (61671274, 61573219, 61701281,61703234), China Postdoctoral Science Foundation (2016M592190,2018M632674), Shandong Provincial Key Research and Development Plan (2017CXGC1504), Shandong Provincial Natural Science Foundation (ZR2017QF009), Shandong Provincial High College Science and Technology Plan (J17KB161), Project of Shandong Province Higher Educational Science and Technology Program (J17KA065), and the Fostering Project of Dominant Discipline and Talent Team of Shandong Province Higher Education Institutions.
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Guo, J., Nie, X., Cui, C., Xi, X., Ma, Y., Yin, Y. (2018). Getting More from One Attractive Scene: Venue Retrieval in Micro-videos. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11164. Springer, Cham. https://doi.org/10.1007/978-3-030-00776-8_66
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