It is intuitive that NLP tasks for logographic languages like Chinese should
benefit from the use of the glyph information in those languages. However, due
to the lack of rich pictographic evidence in glyphs and the weak generalization
ability of standard computer vision models on character data, an effective way
to utilize the glyph information remains to be found. In this paper, we address
this gap by presenting Glyce, the glyph-vectors for Chinese character
representations. We make three major innovations: (1) We use historical Chinese
scripts (e.g., bronzeware script, seal script, traditional Chinese, etc) to
enrich the pictographic evidence in characters; (2) We design CNN structures
(called tianzege-CNN) tailored to Chinese character image processing; and (3)
We use image-classification as an auxiliary task in a multi-task learning setup
to increase the model's ability to generalize. We show that glyph-based models
are able to consistently outperform word/char ID-based models in a wide range
of Chinese NLP tasks. We are able to set new state-of-the-art results for a
variety of Chinese NLP tasks, including tagging (NER, CWS, POS), sentence pair
classification, single sentence classification tasks, dependency parsing, and
semantic role labeling. For example, the proposed model achieves an F1 score of
80.6 on the OntoNotes dataset of NER, +1.5 over BERT; it achieves an almost
perfect accuracy of 99.8\% on the Fudan corpus for text classification. Code
found at this https URL

通过引入历史汉字、设计面向中文的卷积神经网络结构、利用图像分类作为多任务学习的辅助任务，我们提出了基于字形向量的中文字符表示方法 Glyce， 并在中文自然语言处理任务中取得了比基于字或词的模型更好的性能。

Glyce: 用于中文字符表示的字形向量

Glyce: Glyph-vectors for Chinese Character Representations

In this paper, we strive to answer two questions: What is the current state
of 3D hand pose estimation from depth images? And, what are the next challenges
that need to be tackled? Following the successful Hands In the Million
Challenge (HIM2017), we investigate the top 10 state-of-the-art methods on
three tasks: single frame 3D pose estimation, 3D hand tracking, and hand pose
estimation during object interaction. We analyze the performance of different
CNN structures with regard to hand shape, joint visibility, view point and
articulation distributions. Our findings include: (1) isolated 3D hand pose
estimation achieves low mean errors (10 mm) in the view point range of [70,
120] degrees, but it is far from being solved for extreme view points; (2) 3D
volumetric representations outperform 2D CNNs, better capturing the spatial
structure of the depth data; (3) Discriminative methods still generalize poorly
to unseen hand shapes; (4) While joint occlusions pose a challenge for most
methods, explicit modeling of structure constraints can significantly narrow
the gap between errors on visible and occluded joints.

通过 HIM2017 数据集，分析了当前十大最先进的 3D 手势估计方法在三个任务上的表现，探究了不同的 CNN 结构对手的形状、关节可见度、视角和关节分布的表现，结果表明孤立的 3D 手势估计在视角范围 [70,120] 度时的平均误差较低（10 毫米），但对于极端视角的估计还远未完成。