We present an algorithm, Fourier Activity Recognition (FAR), for UAV video
activity recognition. Our formulation uses a novel Fourier object
disentanglement method to innately separate out the human agent (which is
typically small) from the background. Our disentanglement technique operates in
the frequency domain to characterize the extent of temporal change of spatial
pixels, and exploits convolution-multiplication properties of Fourier transform
to map this representation to the corresponding object-background entangled
features obtained from the network. To encapsulate contextual information and
long-range space-time dependencies, we present a novel Fourier Attention
algorithm, which emulates the benefits of self-attention by modeling the
weighted outer product in the frequency domain. Our Fourier attention
formulation uses much fewer computations than self-attention. We have evaluated
our approach on multiple UAV datasets including UAV Human RGB, UAV Human Night,
Drone Action, and NEC Drone. We demonstrate a relative improvement of 8.02% -
38.69% in top-1 accuracy and up to 3 times faster over prior works.

本文介绍了一种针对无人机视频活动识别的算法，名为 Fourier Activity Recognition（FAR），它使用一种新颖的傅里叶对象分离方法，在频域操作以表征空间像素的时间变化程度，并利用傅里叶变换的卷积乘法特性将该表示映射到网络获得的对应的物体背景缠绕特征上，以提取人类活动目标的信息，并通过引入新颖的傅里叶注意力机制，对长期时空关系进行建模，从而达到更好的效果。经评估，该方法在多个 UAV 数据集上实现了 8.02% 至 38.69% 的相对准确度提高以及高达三倍的处理速度提升。

FAR: Fourier 高空视频识别

FAR: Fourier Aerial Video Recognition

Neural end-to-end goal-oriented dialog systems showed promise to reduce the
workload of human agents for customer service, as well as reduce wait time for
users. However, their inability to handle new user behavior at deployment has
limited their usage in real world. In this work, we propose an end-to-end
trainable method for neural goal-oriented dialog systems which handles new user
behaviors at deployment by transferring the dialog to a human agent
intelligently. The proposed method has three goals: 1) maximize user's task
success by transferring to human agents, 2) minimize the load on the human
agents by transferring to them only when it is essential and 3) learn online
from the human agent's responses to reduce human agents load further. We
evaluate our proposed method on a modified-bAbI dialog task that simulates the
scenario of new user behaviors occurring at test time. Experimental results
show that our proposed method is effective in achieving the desired goals.

该研究提出了一种可端到端训练的神经目标导向对话系统方法，能通过智能地将对话转移给人工服务代理来处理新用户行为。该方法的三个目标是：最大化用户任务成功率，最小化对人工服务代理的负担，并通过人工代理的反馈进行在线学习，以进一步减轻其负担。实验结果表明所提出的方法能够有效实现这些目标。