1,681 research outputs found
2kenize: Tying Subword Sequences for Chinese Script Conversion
Simplified Chinese to Traditional Chinese character conversion is a common
preprocessing step in Chinese NLP. Despite this, current approaches have poor
performance because they do not take into account that a simplified Chinese
character can correspond to multiple traditional characters. Here, we propose a
model that can disambiguate between mappings and convert between the two
scripts. The model is based on subword segmentation, two language models, as
well as a method for mapping between subword sequences. We further construct
benchmark datasets for topic classification and script conversion. Our proposed
method outperforms previous Chinese Character conversion approaches by 6 points
in accuracy. These results are further confirmed in a downstream application,
where 2kenize is used to convert pretraining dataset for topic classification.
An error analysis reveals that our method's particular strengths are in dealing
with code-mixing and named entities.Comment: Accepted to ACL 202
Fast Deep Matting for Portrait Animation on Mobile Phone
Image matting plays an important role in image and video editing. However,
the formulation of image matting is inherently ill-posed. Traditional methods
usually employ interaction to deal with the image matting problem with trimaps
and strokes, and cannot run on the mobile phone in real-time. In this paper, we
propose a real-time automatic deep matting approach for mobile devices. By
leveraging the densely connected blocks and the dilated convolution, a light
full convolutional network is designed to predict a coarse binary mask for
portrait images. And a feathering block, which is edge-preserving and matting
adaptive, is further developed to learn the guided filter and transform the
binary mask into alpha matte. Finally, an automatic portrait animation system
based on fast deep matting is built on mobile devices, which does not need any
interaction and can realize real-time matting with 15 fps. The experiments show
that the proposed approach achieves comparable results with the
state-of-the-art matting solvers.Comment: ACM Multimedia Conference (MM) 2017 camera-read
Open Set Chinese Character Recognition using Multi-typed Attributes
Recognition of Off-line Chinese characters is still a challenging problem,
especially in historical documents, not only in the number of classes extremely
large in comparison to contemporary image retrieval methods, but also new
unseen classes can be expected under open learning conditions (even for CNN).
Chinese character recognition with zero or a few training samples is a
difficult problem and has not been studied yet. In this paper, we propose a new
Chinese character recognition method by multi-type attributes, which are based
on pronunciation, structure and radicals of Chinese characters, applied to
character recognition in historical books. This intermediate attribute code has
a strong advantage over the common `one-hot' class representation because it
allows for understanding complex and unseen patterns symbolically using
attributes. First, each character is represented by four groups of attribute
types to cover a wide range of character possibilities: Pinyin label, layout
structure, number of strokes, three different input methods such as Cangjie,
Zhengma and Wubi, as well as a four-corner encoding method. A convolutional
neural network (CNN) is trained to learn these attributes. Subsequently,
characters can be easily recognized by these attributes using a distance metric
and a complete lexicon that is encoded in attribute space. We evaluate the
proposed method on two open data sets: printed Chinese character recognition
for zero-shot learning, historical characters for few-shot learning and a
closed set: handwritten Chinese characters. Experimental results show a good
general classification of seen classes but also a very promising generalization
ability to unseen characters.Comment: 29 pages, submitted to Pattern Recognitio
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