Abstract
The recent success of the generative model shows that leveraging the multi-modal embedding space can manipu-late an image using text information. However, manipulating an image with other sources rather than text, such as sound, is not easy due to the dynamic characteristics of the sources. Especially, sound can convey vivid emotions and dynamic expressions of the real world. Here, we propose a framework that directly encodes sound into the multi-modal (image-text) embedding space and manipulates an image from the space. Our audio encoder is trained to pro-duce a latent representation from an audio input, which is forced to be aligned with image and text representations in the multi-modal embedding space. We use a direct latent op-timization method based on aligned embeddings for sound-guided image manipulation. We also show that our method can mix different modalities, i.e., text and audio, which en-rich the variety of the image modification. The experiments on zero-shot audio classification and semantic-level image classification show that our proposed model outperforms other text and sound-guided state-of-the-art methods.
Original language | English |
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Title of host publication | Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 |
Publisher | IEEE Computer Society |
Pages | 3367-3376 |
Number of pages | 10 |
ISBN (Electronic) | 9781665469463 |
DOIs | |
Publication status | Published - 2022 |
Event | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 - New Orleans, United States Duration: 2022 Jun 19 → 2022 Jun 24 |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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Volume | 2022-June |
ISSN (Print) | 1063-6919 |
Conference
Conference | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 |
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Country/Territory | United States |
City | New Orleans |
Period | 22/6/19 → 22/6/24 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- Image and video synthesis and generation
- Self-& semi-& meta- & unsupervised learning
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition