The booming development and huge market of micro-videos bring new e-commerce
channels for merchants. Currently, more micro-video publishers prefer to embed
relevant ads into their micro-videos, which not only provides them with
business income but helps the audiences to discover their interesting products.
However, due to the micro-video recording by unprofessional equipment,
involving various topics and including multiple modalities, it is challenging
to locate the products related to micro-videos efficiently, appropriately, and
accurately. We formulate the microvideo-product retrieval task, which is the
first attempt to explore the retrieval between the multi-modal and multi-modal
instances.
A novel approach named Multi-Queue Momentum Contrast (MQMC) network is
proposed for bidirectional retrieval, consisting of the uni-modal feature and
multi-modal instance representation learning. Moreover, a discriminative
selection strategy with a multi-queue is used to distinguish the importance of
different negatives based on their categories. We collect two large-scale
microvideo-product datasets (MVS and MVS-large) for evaluation and manually
construct the hierarchical category ontology, which covers sundry products in
daily life. Extensive experiments show that MQMC outperforms the
state-of-the-art baselines. Our replication package (including code, dataset,
etc.) is publicly available at this https URL

通过提出 MQMC 网络，该文首次尝试探索多模式和多模态实例之间的检索问题，为商家提供了新的电子商务渠道，并成功地解决了微视频记录的挑战，取得了优秀的性能结果。

多队列动量对比用于微视频产品检索

Multi-queue Momentum Contrast for Microvideo-Product Retrieval

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.

本文分析了一个带有 5.8 万个标签的新颖的微视频数据集，并介绍了针对最先进的运动和深度视觉特征进行定义的视点特定和时空演化模型，得出结论我们的数据集为大规模视频分析，新颖的视点和开放世界动态等领域开辟了新的研究机会。

微视频的开放世界

The Open World of Micro-Videos

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.

研究了微视频的创造性，旨在尝试理解构成一个有创造性的视频的特征以及解决自动检测有创造性内容的问题。通过提出一组计算特征，并且进行了分析来确定这些特征与创意视频之间的相关性，并通过监督方法进行自动检测的评估，最终取得了不错的结果，表明了成果的创造性与美学价值和新颖性模型化的必要性。