# Unsupervised Voice-Face Representation Learning by Cross-Modal Prototype Contrast

arXiv: https://arxiv.org/abs/2204.14057 · HTML: https://mlsanity.com/paper/2204.14057/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 44 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [Prototypical Contrastive Learning of Unsupervised Representations](https://mlsanity.com/paper/2005.04966/) (2020)
- [Hard Negative Mixing for Contrastive Learning](https://mlsanity.com/paper/2010.01028/) (2020)
- [Debiased Contrastive Learning](https://mlsanity.com/paper/2007.00224/) (2020)
- [Self-Supervised MultiModal Versatile Networks](https://mlsanity.com/paper/2006.16228/) (2020)
- [Self-Supervised Learning by Cross-Modal Audio-Video Clustering](https://mlsanity.com/paper/1911.12667/) (2019)
- [Audio-Visual Instance Discrimination with Cross-Modal Agreement](https://mlsanity.com/paper/2004.12943/) (2020)
- [VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples](https://mlsanity.com/paper/2103.05905/) (2021)
- [Memory-augmented Dense Predictive Coding for Video Representation Learning](https://mlsanity.com/paper/2008.01065/) (2020)
- [Kernel Operations on the GPU, with Autodiff, without Memory Overflows](https://mlsanity.com/paper/2004.11127/) (2020)
- [Online Deep Clustering for Unsupervised Representation Learning](https://mlsanity.com/paper/2006.10645/) (2020)
- [Robust Audio-Visual Instance Discrimination](https://mlsanity.com/paper/2103.15916/) (2021)
- [CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations](https://mlsanity.com/paper/2109.14910/) (2021)
- [Seeing Voices and Hearing Faces: Cross-modal biometric matching](https://mlsanity.com/paper/1804.00326/) (2018)
- [Learnable PINs: Cross-Modal Embeddings for Person Identity](https://mlsanity.com/paper/1805.00833/) (2018)
- [Conditional Negative Sampling for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2010.02037/) (2020)
- [On Learning Associations of Faces and Voices](https://mlsanity.com/paper/1805.05553/) (2018)
- [Cross-modal Speaker Verification and Recognition: A Multilingual Perspective](https://mlsanity.com/paper/2004.13780/) (2020)
- [FaceFilter: Audio-visual speech separation using still images](https://mlsanity.com/paper/2005.07074/) (2020)
- [Disjoint Mapping Network for Cross-modal Matching of Voices and Faces](https://mlsanity.com/paper/1807.04836/) (2018)
- [Seeking the Shape of Sound: An Adaptive Framework for Learning Voice-Face Association](https://mlsanity.com/paper/2103.07293/) (2021)
- [From Inference to Generation: End-to-end Fully Self-supervised Generation of Human Face from Speech](https://mlsanity.com/paper/2004.05830/) (2020)

## Built on by (most cited first, 6 of 6)

- [Learning Branched Fusion and Orthogonal Projection for Face-Voice Association](https://mlsanity.com/paper/2208.10238/) (2022)
- [Exploring Robust Face-Voice Matching in Multilingual Environments](https://mlsanity.com/paper/2407.19875/) (2024)
- [Contrastive Learning-based Chaining-Cluster for Multilingual Voice-Face Association](https://mlsanity.com/paper/2408.02025/) (2024)
- [Fuse after Align: Improving Face-Voice Association Learning via Multimodal Encoder](https://mlsanity.com/paper/2404.09509/) (2024)
- [SSAVSV: Towards Unified Model for Self-Supervised Audio-Visual Speaker Verification](https://mlsanity.com/paper/2506.17694/) (2025)
- [Face and Voice Cross-modal Association with Learning Convex Feature Embedding](https://mlsanity.com/paper/2607.28129/) (2026)

## Abstract (arXiv)

We present an approach to learn voice-face representations from the talking face videos, without any identity labels. Previous works employ cross-modal instance discrimination tasks to establish the correlation of voice and face. These methods neglect the semantic content of different videos, introducing false-negative pairs as training noise. Furthermore, the positive pairs are constructed based on the natural correlation between audio clips and visual frames. However, this correlation might be weak or inaccurate in a large amount of real-world data, which leads to deviating positives into the contrastive paradigm. To address these issues, we propose the cross-modal prototype contrastive learning (CMPC), which takes advantage of contrastive methods and resists adverse effects of false negatives and deviate positives. On one hand, CMPC could learn the intra-class invariance by constructing semantic-wise positives via unsupervised clustering in different modalities. On the other hand, by comparing the similarities of cross-modal instances from that of cross-modal prototypes, we dynamically recalibrate the unlearnable instances' contribution to overall loss. Experiments show that the proposed approach outperforms state-of-the-art unsupervised methods on various voice-face association evaluation protocols. Additionally, in the low-shot supervision setting, our method also has a significant improvement compared to previous instance-wise contrastive learning.