Multi-Modal Learning: Study on A Large-Scale Micro-Video Data Collection

Abstract

Micro-video sharing social services, as a new phenomenon in social media, enable users to share micro-videos and thus gain increasing enthusiasm among people. One distinct characteristic of micro-videos is the multi-modality, as these videos always have visual signals, audio tracks, textual descriptions as well as social clues. Such multi-modality data makes it possible to obtain a comprehensive understanding of videos and hence provides new opportunities for researchers. However, limited efforts thus far have been dedicated to this new emerging user-generated contents (UGCs) due to the lack of large-scale benchmark dataset. Towards this end, in this paper, we construct a large-scale micro-video dataset, which can support many research domains, such as popularity prediction and venue estimation. Based upon this dataset, we conduct an initial study in popularity prediction of micro-videos. Finally, we identify our future work.