The Open World of Micro-Videos

By Phuc Xuan Nguyen, Grégory Rogez, Charless C. Fowlkes, Deva Ramanan. 2016

38 citations recorded by the source corpus.

Micro-videos are six-second videos popular on social media networks with several unique properties. Firstly, because of the authoring process, they contain significantly more diversity and narrative structure than existing collections of video "snippets". Secondly, because they are often captured by hand-held mobile cameras, they contain specialized viewpoints including third-person, egocentric, and self-facing views seldom seen in traditional produced video. Thirdly, due to to their continuous production and publication on social networks, aggregate micro-video content contains interesting open-world dynamics that reflects the temporal evolution of tag topics. These aspects make micro-videos an appealing well of visual data for developing large-scale models for video understanding. We analyze a novel dataset of micro-videos labeled with 58 thousand tags. To analyze this data, we introduce viewpoint-specific and temporally-evolving models for video understanding, defined over state-of-the-art motion and deep visual features. We conclude that our dataset opens up new research opportunities for large-scale video analysis, novel viewpoints, and open-world dynamics.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Generating Videos with Scene Dynamics (2016, background): “Finally, this paper is related to a growing body of work that capitalizes on large amounts of unlabeled video for visual recognition tasks [18, 47, 37, 13, 24, 25, 3, 32, 26, 27, 19, 41, 42, 1].”
SoundNet: Learning Sound Representations from Unlabeled Video (2016, background): “Cross-Modal Learning and Unlabeled Video: Our approach is broadly inspired by efforts to model cross-modal relations [24, 14, 7, 26] and works that leverage large amounts of unlabeled video [25, 41, 8, 40, 39].”
Weakly-Supervised Action Localization With Background Modeling (2019, methodology): “Many approaches to fully-supervised action localization leverage these annotations and adopt a two-stage, propose-thenclassification framework [2, 26, 7, 14, 24, 37].”
Oops! Predicting Unintentional Action in Video (2019, background): “Critical to this success are datasets of diverse videos released to facilitate this research [50, 5, 32, 53, 64, 27, 7, 1, 41, 51, 28, 17, 16, 11, 18, 40].”