The need for efficiently finding the video content a user wants is increasing
because of the erupting of user-generated videos on the Web. Existing
keyword-based or content-based video retrieval methods usually determine what
occurs in a video but not when and where. In this paper, we make an answer to
the question of when and where by formulating a new task, namely
spatio-temporal video re-localization. Specifically, given a query video and a
reference video, spatio-temporal video re-localization aims to localize
tubelets in the reference video such that the tubelets semantically correspond
to the query. To accurately localize the desired tubelets in the reference
video, we propose a novel warp LSTM network, which propagates the
spatio-temporal information for a long period and thereby captures the
corresponding long-term dependencies. Another issue for spatio-temporal video
re-localization is the lack of properly labeled video datasets. Therefore, we
reorganize the videos in the AVA dataset to form a new dataset for
spatio-temporal video re-localization research. Extensive experimental results
show that the proposed model achieves superior performances over the designed
baselines on the spatio-temporal video re-localization task.

提出了一种新的任务，即时空视频重新定位，对缺乏适当标记的视频数据组织为新数据集，以解决现有方法通常仅确定视频中发生的事情而不是时间和位置的问题，并通过提出一种新的 Warp LSTM 网络来准确定位参考视频中的所需视频区域。该方法在时空视频重新定位任务中的表现优于设计的基线。

Warp LSTM 实现时空视频重新定位

Spatio-temporal Video Re-localization by Warp LSTM

To understand the world, we humans constantly need to relate the present to
the past, and put events in context. In this paper, we enable existing video
models to do the same. We propose a long-term feature bank---supportive
information extracted over the entire span of a video---to augment
state-of-the-art video models that otherwise would only view short clips of 2-5
seconds. Our experiments demonstrate that augmenting 3D convolutional networks
with a long-term feature bank yields state-of-the-art results on three
challenging video datasets: AVA, EPIC-Kitchens, and Charades.

本文介绍了一种使用长期特征库的方法，用于增强现有的视频模型，将传统视野仅在 2-5 秒短片内的限制打破，实现了在 AVA、EPIC-Kitchens、Charades 这三个数据集上领先水平的效果。

长期特征库用于详细视频理解

Long-Term Feature Banks for Detailed Video Understanding

Image aesthetics assessment has been challenging due to its subjective
nature. Inspired by the scientific advances in the human visual perception and
neuroaesthetics, we design Brain-Inspired Deep Networks (BDN) for this task.
BDN first learns attributes through the parallel supervised pathways, on a
variety of selected feature dimensions. A high-level synthesis network is
trained to associate and transform those attributes into the overall aesthetics
rating. We then extend BDN to predicting the distribution of human ratings,
since aesthetics ratings are often subjective. Another highlight is our
first-of-its-kind study of label-preserving transformations in the context of
aesthetics assessment, which leads to an effective data augmentation approach.
Experimental results on the AVA dataset show that our biological inspired and
task-specific BDN model gains significantly performance improvement, compared
to other state-of-the-art models with the same or higher parameter capacity.

本论文使用脑启发式深度网络来进行图像美学评价，并进行了标签保留转换的首次研究，以及对人类评级的分布预测。在 AVA 数据集上的实验结果表明，相较于其他同等或更高参数容量的最新技术模型，我们的生物学启示和专门的 BDN 模型获得了显著的性能提升。