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Remote Sensing Image Generation From Audio
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AI
DOI：10.1109/LGRS.2020.2992324.png)
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摘要
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Generating image from other modal data has attracted much attention in cross-modal studies, since the generated image offers intuitive vision information. Unlike the previous works which generate an image from text, a novel task is introduced, generating an image from audio. However, semantic gap intrinsically exists in cross-modal data, which disturbs the generative results. In order to explore the relevance between the audio and image, a novel reranking audio-image translation method is proposed. The proposed method: 1) maps the audio and image into a uniform feature space; 2) designs an audio-audio matching network to match the related audio; and 3) adopts an audio-image matching network for every matched audio to generate a related image, and the most frequent image is voted as the final result. Extensive experiments on two remote sensing cross-modal data sets demonstrate that the proposed method can visualize the content of audio.
Keyword:
Remote sensing
Semantics
Feature extraction
Gallium nitride
Neural networks
Sensors
Mel frequency cepstral coefficient
Cross-modal
generation
reranking
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
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