You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: This paper presents a degree-of-loop assessment method for microvideo clips. Loop video is one of the popular features in microvideo, but there are so many non-loop video tagged with “loop” on microvideo services. This is because upload-ers or spammers also know that loop video is popular and they want to draw attention from viewers. In this paper, we statistically analyze the scene dynamics of the video by using color, optical flow, saliency maps, and evaluate the degree-of-loop. We have collected more than 1,000 video clips from Vine and subjectively evaluated their degree-of-loop. Experimental results show that our proposed algorithm can classify loop non-loop video with 85.7 accuracy and categorize them into five degree-of-loop categories with 61.5 accuracy.

[2]: The notion of creativity, as opposed to related concepts such as beauty or interestingness, has not been studied from the perspective of automatic analysis of multimedia content. Meanwhile, short online videos shared on social media platforms, or micro-videos, have arisen as a new medium for creative expression. In this paper we study creative micro-videos in an effort to understand the features that make a video creative, and to address the problem of automatic detection of creative content. Defining creative videos as those that are novel and have aesthetic value, we conduct a crowdsourcing experiment to create a dataset of over 3, 800 micro-videos labelled as creative and non-creative. We propose a set of computational features that we map to the components of our definition of creativity, and conduct an analysis to determine which of these features correlate most with creative video. Finally, we evaluate a supervised approach to automatically detect creative video, with promising results, showing that it is necessary to model both aesthetic value and novelty to achieve optimal classification accuracy.
There is little existing work on micro-video analysis, as the medium itself is new. Redi [2] explore the problem of finding creative micro-videos, inspired by similar studies of image quality assessment. Sano [1] analyze the problem of detecting loop micro-videos (that are designed to be played in a continuous six-second loop). Here we focus on analyzing general properties of micro-videos, with the explicit goal of constructing a new, large-scale benchmark for temporally-evolving tag prediction.