Spatio-temporal feature encoding is essential for encoding facial expression
dynamics in video sequences. At test time, most spatio-temporal encoding
methods assume that a temporally segmented sequence is fed to a learned model,
which could require the prediction to wait until the full sequence is available
to an auxiliary task that performs the temporal segmentation. This causes a
delay in predicting the expression. In an interactive setting, such as
affective interactive agents, such delay in the prediction could not be
tolerated. Therefore, training a model that can accurately predict the facial
expression "on-the-fly" (as they are fed to the system) is essential. In this
paper, we propose a new spatio-temporal feature learning method, which would
allow prediction with partial sequences. As such, the prediction could be
performed on-the-fly. The proposed method utilizes an estimated expression
intensity to generate dense labels, which are used to regulate the prediction
model training with a novel objective function. As results, the learned
spatio-temporal features can robustly predict the expression with partial
(incomplete) expression sequences, on-the-fly. Experimental results showed that
the proposed method achieved higher recognition rates compared to the
state-of-the-art methods on both datasets. More importantly, the results
verified that the proposed method improved the prediction frames with partial
expression sequence inputs.

研究人员提出了一种新的面部表情分析方法，该方法采用估计的表情强度生成密集标签，并使用一种新的目标函数调节预测模型训练，使得学习到的时空特征能够在不完整的表情序列输入状态下进行精确预测。结果表明，该方法在数据集上比现有方法提高了预测率，并改善了部分表情序列输入帧的预测效果

利用部分表情序列学习时空特征进行即时预测

Learning Spatio-temporal Features with Partial Expression Sequences for  on-the-Fly Prediction

A catastrophic forgetting problem makes deep neural networks forget the
previously learned information, when learning data collected in new
environments, such as by different sensors or in different light conditions.
This paper presents a new method for alleviating the catastrophic forgetting
problem. Unlike previous research, our method does not use any information from
the source domain. Surprisingly, our method is very effective to forget less of
the information in the source domain, and we show the effectiveness of our
method using several experiments. Furthermore, we observed that the forgetting
problem occurs between mini-batches when performing general training processes
using stochastic gradient descent methods, and this problem is one of the
factors that degrades generalization performance of the network. We also try to
solve this problem using the proposed method. Finally, we show our
less-forgetting learning method is also helpful to improve the performance of
deep neural networks in terms of recognition rates.

研究神经网络中的遗忘问题，提出了一种不同于以往的方法来解决该问题，且不会使用源域的信息，此方法在减少遗忘源域信息方面非常有效，同时实验表明使用该方法可以提高网络在识别率等方面的性能。

深度神经网络的少遗忘学习

Less-forgetting Learning in Deep Neural Networks

Our Multi-Column Deep Neural Networks achieve best known recognition rates on
Chinese characters from the ICDAR 2011 and 2013 offline handwriting
competitions, approaching human performance.

本文介绍了使用多列深度神经网络实现对 2011 年和 2013 年 ICDAR 离线手写中文字符识别，达到接近人类水平的最佳识别率。