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Improving Text Encoding for Retro-Remote Sensing
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DOI：10.1109/LGRS.2020.2983851.png)
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Abstract
En 中文
A recent work on retro-remote sensing (converting ancient text descriptions into images) was proposed using a multilabel encoding scheme in which an input text description is represented by a binary vector indicating the presence or absence of specific objects. However, this kind of encoding disregards information such as object attributes and spatial relationship between multiple objects in a description, resulting in images that do not semantically (fully) conform to the input description. In this letter, we propose an improved text-encoding mechanism that takes into account different levels of information available from an input text. The encoded text is then used as conditional information to guide the image synthesis process using generative adversarial networks (GANs). Besides, we present a modified GAN architecture intending to improve the semantic content of the generated images. Both the qualitative and quantitative results obtained indicate that the proposed method is particularly promising.
Keywords:
Gallium nitride
Training
Generators
Generative adversarial networks
Context modeling
Encoding
Sensors
Generative adversarial networks (GANs)
multimodal learning
retro-remote sensing
text-to-image synthesis
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