Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automatic Graphics Program Generation using Attention-Based Hierarchical
  Decoder   Recent progress on deep learning has made it possible to automatically
transform the screenshot of Graphic User Interface (GUI) into code by using the
encoder-decoder framework. While the commonly adopted image encoder (e.g., CNN
network), might be capable of extracting image features to the desired level,
interpreting these abstract image features into hundreds of tokens of code puts
a particular challenge on the decoding power of the RNN-based code generator.
Considering the code used for describing GUI is usually hierarchically
structured, we propose a new attention-based hierarchical code generation
model, which can describe GUI images in a finer level of details, while also
being able to generate hierarchically structured code in consistency with the
hierarchical layout of the graphic elements in the GUI. Our model follows the
encoder-decoder framework, all the components of which can be trained jointly
in an end-to-end manner. The experimental results show that our method
outperforms other current state-of-the-art methods on both a publicly available
GUI-code dataset as well as a dataset established by our own.

17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th