We introduce the concept of "dynamic image", a novel compact representation
of videos useful for video analysis, particularly in combination with
convolutional neural networks (CNNs). A dynamic image encodes temporal data
such as RGB or optical flow videos by using the concept of `rank pooling'. The
idea is to learn a ranking machine that captures the temporal evolution of the
data and to use the parameters of the latter as a representation. When a linear
ranking machine is used, the resulting representation is in the form of an
image, which we call dynamic because it summarizes the video dynamics in
addition of appearance. This is a powerful idea because it allows to convert
any video to an image so that existing CNN models pre-trained for the analysis
of still images can be immediately extended to videos. We also present an
efficient and effective approximate rank pooling operator, accelerating
standard rank pooling algorithms by orders of magnitude, and formulate that as
a CNN layer. This new layer allows generalizing dynamic images to dynamic
feature maps. We demonstrate the power of the new representations on standard
benchmarks in action recognition achieving state-of-the-art performance.

本文介绍了动态图像的概念，它是一种新型的紧凑表示方法，可用于视频分析，特别是与卷积神经网络（CNNs）相结合。它允许将任何视频转换为图片，从而可以立即将现有的针对静态图像分析的 CNN 模型扩展到视频，并在动作识别等标准基准测试中实现了最先进的性能。

动态图像网络在动作识别中的应用

Action Recognition with Dynamic Image Networks

We propose a function-based temporal pooling method that captures the latent
structure of the video sequence data - e.g. how frame-level features evolve
over time in a video. We show how the parameters of a function that has been
fit to the video data can serve as a robust new video representation. As a
specific example, we learn a pooling function via ranking machines. By learning
to rank the frame-level features of a video in chronological order, we obtain a
new representation that captures the video-wide temporal dynamics of a video,
suitable for action recognition. Other than ranking functions, we explore
different parametric models that could also explain the temporal changes in
videos. The proposed functional pooling methods, and rank pooling in
particular, is easy to interpret and implement, fast to compute and effective
in recognizing a wide variety of actions. We evaluate our method on various
benchmarks for generic action, fine-grained action and gesture recognition.
Results show that rank pooling brings an absolute improvement of 7-10 average
pooling baseline. At the same time, rank pooling is compatible with and
complementary to several appearance and local motion based methods and
features, such as improved trajectories and deep learning features.

提出了一种函数为基础的时间汇集方法，该方法可捕获视频序列数据的潜在结构，并生成一个新的视频表示形式以适用于动作识别问题。结果表明，对于泛化的动作识别问题，精细化的动作识别问题和手势识别问题等，此方法都能够显著提高平均汇集基线的绝对值 7-10。