Popularity Prediction for Social Media over Arbitrary Time Horizons
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
British Machine Vision Conference (BMVC)
Many of today’s most successful video segmentation methods use long-term feature trajectories as their first processing step. Such methods typically use spectral clustering to segment these trajectories, implicitly assuming that motion is translational in image space. In this paper, we explore the idea of explicitly fitting more general motion models in order to classify trajectories as foreground or background. We find that homographies are sufficient to model a wide variety of background motions found in real-world videos. Our simple approach achieves competitive performance on the DAVIS benchmark, while using techniques complementary to state-of-the-art approaches.
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
Liqi Yan, Qifan Wang, Yiming Cu, Fuli Feng, Xiaojun Quan, Xiangyu Zhang, Dongfang Liu
Barlas Oğuz, Kushal Lakhotia, Anchit Gupta, Patrick Lewis, Vladimir Karpukhin, Aleksandra Piktus, Xilun Chen, Sebastian Riedel, Wen-tau Yih, Sonal Gupta, Yashar Mehdad