# Cross-Modal Contrastive Representation Learning for Audio-to-Image Generation

arXiv: https://arxiv.org/abs/2207.12121 · HTML: https://mlsanity.com/paper/2207.12121/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 7 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Deep Cross-Modal Audio-Visual Generation](https://mlsanity.com/paper/1704.08292/) (2017)
- [CMCGAN: A Uniform Framework for Cross-Modal Visual-Audio Mutual Generation](https://mlsanity.com/paper/1711.08102/) (2017)
- [CLAR: Contrastive Learning of Auditory Representations](https://mlsanity.com/paper/2010.09542/) (2020)
- [Cascade Attention Guided Residue Learning GAN for Cross-Modal Translation](https://mlsanity.com/paper/1907.01826/) (2019)

## Built on by (most cited first, 2 of 2)

- [OneEncoder: A Lightweight Framework for Progressive Alignment of Modalities](https://mlsanity.com/paper/2409.11059/) (2024)
- [Task-based Loss Functions in Computer Vision: A Comprehensive Review](https://mlsanity.com/paper/2504.04242/) (2025)

## Abstract (arXiv)

Multiple modalities for certain information provide a variety of perspectives on that information, which can improve the understanding of the information. Thus, it may be crucial to generate data of different modality from the existing data to enhance the understanding. In this paper, we investigate the cross-modal audio-to-image generation problem and propose Cross-Modal Contrastive Representation Learning (CMCRL) to extract useful features from audios and use it in the generation phase. Experimental results show that CMCRL enhances quality of images generated than previous research.