“中文分词是自然语言处理领域的基础工作, 然而前人的医学文本分词工作都只是直接套用通用分词的方法, 而医学文本多专用术语的特点让分词系统需要对医学专用术语和医学文本中的非医学术语文本提供不同的分词粒度。本文提出了双编码器医学文本中文分词模型, 利用辅助编码器为医学专有术语提供粗粒度表示。模型将需要粗粒度分词的医学专用术语和需要通用分词粒度的文本分开, 在提升医学专用术语的分词能力的同时最大限度地避免了其粗粒度对于医学文本中通用文本分词的干扰。”
The F-Encoder and T-Encoder model the correlations within frequency bands and time frames, respectively, and they are embedded into a time-frequency joint learning strategy to obtain the time-frequency patterns for speech emotions.
In this paper, we present a large-scale, multi-source, and unconstrained database called SDFE-LV for spotting the onset and offset frames of a complete dynamic facial expression from long videos, which is known as the topic of dynamic facial expression spotting (DFES) and a vital prior step for lots of facial expression analysis tasks.
To solve these problems, this paper proposes a novel Transfer Group Sparse Regression method, namely TGSR, which aims to 1) optimize the measurement and better alleviate the difference between the source and target databases, and 2) highlight the valid facial regions to enhance extracted features, by the operation of selecting the group features from the raw face feature, where each region is associated with a group of raw face feature, i. e., the salient facial region selection.
Correctly perceiving micro-expression is difficult since micro-expression is an involuntary, repressed, and subtle facial expression, and efficiently revealing the subtle movement changes and capturing the significant segments in a micro-expression sequence is the key to micro-expression recognition (MER).
In this paper, we propose a spatial transformer point convolution (STPC) method to achieve anisotropic convolution filtering on point clouds.
Experimental results show that DFEW is a well-designed and challenging database, and the proposed EC-STFL can promisingly improve the performance of existing spatiotemporal deep neural networks in coping with the problem of dynamic FER in the wild.
Cross-database micro-expression recognition (CDMER) is one of recently emerging and interesting problem in micro-expression analysis.
Cross-database non-frontal expression recognition is a very meaningful but rather difficult subject in the fields of computer vision and affect computing.
In this paper, we investigate the cross-database micro-expression recognition problem, where the training and testing samples are from two different micro-expression databases.
Then a bi-directional temporal RNN layer is further used to learn discriminative temporal dependencies from the sequences concatenating spatial features of each time slice produced from the spatial RNN layer.