Inspired by human neurological structures for action anticipation, we present
an action anticipation model that enables the prediction of plausible future
actions by forecasting both the visual and temporal future. In contrast to
current state-of-the-art methods which first learn a model to predict future
video features and then perform action anticipation using these features, the
proposed framework jointly learns to perform the two tasks, future visual and
temporal representation synthesis, and early action anticipation. The joint
learning framework ensures that the predicted future embeddings are informative
to the action anticipation task. Furthermore, through extensive experimental
evaluations we demonstrate the utility of using both visual and temporal
semantics of the scene, and illustrate how this representation synthesis could
be achieved through a recurrent Generative Adversarial Network (GAN) framework.
Our model outperforms the current state-of-the-art methods on multiple
datasets: UCF101, UCF101-24, UT-Interaction and TV Human Interaction.

本篇论文提出了一种行动预测模型，受人类神经结构的启发，在预测未来视觉和时间表现方面，能够预测合理的未来行动，并证明使用视觉和时间语义的场景表示结合递归生成对抗网络 (GAN) 框架可以实现未来表现的表示综合，该模型在多个数据集上优于当前最先进的方法。

预测未来：联合学习模型用于行动预测

Predicting the Future: A Jointly Learnt Model for Action Anticipation

Generative Adversarial Networks (GANs) have shown remarkable success as a
framework for training models to produce realistic-looking data. In this work,
we propose a Recurrent GAN (RGAN) and Recurrent Conditional GAN (RCGAN) to
produce realistic real-valued multi-dimensional time series, with an emphasis
on their application to medical data. RGANs make use of recurrent neural
networks in the generator and the discriminator. In the case of RCGANs, both of
these RNNs are conditioned on auxiliary information. We demonstrate our models
in a set of toy datasets, where we show visually and quantitatively (using
sample likelihood and maximum mean discrepancy) that they can successfully
generate realistic time-series. We also describe novel evaluation methods for
GANs, where we generate a synthetic labelled training dataset, and evaluate on
a real test set the performance of a model trained on the synthetic data, and
vice-versa. We illustrate with these metrics that RCGANs can generate
time-series data useful for supervised training, with only minor degradation in
performance on real test data. This is demonstrated on digit classification
from 'serialised' MNIST and by training an early warning system on a medical
dataset of 17,000 patients from an intensive care unit. We further discuss and
analyse the privacy concerns that may arise when using RCGANs to generate
realistic synthetic medical time series data.

本文提出使用循环神经网络的生成对抗网络 (GAN) 框架来生成逼真多维时间序列数据，尤其聚焦于应用在医疗数据上。通过对玩具数据集的试验，本文证明了所提出的 RGAN 和 RCGAN 可以成功地生成逼真的时间序列数据，并在手写数字分类和医疗数据的早期预警系统等方面展示了它们在监督训练任务中的可用性。同时，本文还探讨了利用 RCGAN 生成逼真医学时间序列数据可能引起的隐私问题。