Improving Face-Voice Association Learning through Multimodal Encoding and Effective Pair Selection

Core Concepts

The paper proposes the Fuse after Align (FAA) framework, which uses a multimodal encoder to learn cross-modal relations between faces and voices more effectively. It also introduces an effective pair selection method to enhance the diversity and difficulty of training samples, leading to improved performance in voice-face matching, verification, and retrieval tasks.

Abstract

The paper addresses the problem of learning the association between voice and face, which is important for applications like virtual reality, criminal investigations, and multimodal information retrieval. Previous works have relied on cosine similarity or L2 distance to evaluate the likeness of voices and faces, which only considers the embeddings as high-dimensional vectors and utilizes a minimal scope of available information. The key contributions of the paper are: Proposing the use of a multimodal encoder to learn cross-modal relations from a deeper and more diverse perspective, going beyond just optimizing similarity metrics. Employing a mixed training objective, combining modality alignment through contrastive learning and direct cross-modality learning through face-voice matching. Introducing an effective pair selection method, combining diverse positive pair selection and hard negative mining, to enhance the diversity and difficulty of training samples. The paper first describes the dual-modality pooling and progressive clustering techniques used to obtain pseudo-labels for the unlabeled data. It then details the two training objectives: Face-Voice Contrastive Learning: Uses the multi-similarity loss to learn better unimodal representations before fusion. Face-Voice Matching: Trains the multimodal encoder to predict whether a face-voice pair belongs to the same identity or not. The experiments show that the proposed FAA framework outperforms previous state-of-the-art methods in voice-face matching, verification, and retrieval tasks, demonstrating the effectiveness of the multimodal encoder and the pair selection approach.

Stats

The dataset used for training and evaluation is the VoxCeleb dataset, which contains 153K audio clips and 1.2M face images of 1225 identities. The training, validation, and test sets contain 16,650, 2,045, and 3,428 videos, respectively.

Quotes

None.

Key Insights Distilled From

Fuse after Align: Improving Face-Voice Association Learning via Multimodal Encoder

by Chong Peng,L... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09509.pdf

Deeper Inquiries

How could the proposed FAA framework be extended to handle more than two modalities, such as incorporating text or other contextual information

The FAA framework could be extended to handle more than two modalities by incorporating text or other contextual information through a multimodal fusion approach. This extension would involve integrating additional encoders for processing text data or other modalities, such as video or sensor data. The fusion process would combine the outputs of these different modalities into a unified representation space, allowing for comprehensive cross-modal learning. By leveraging techniques like attention mechanisms or graph neural networks, the framework could effectively capture complex relationships between multiple modalities and enhance the overall association learning process.

What are the potential limitations or failure cases of the multimodal encoder approach, and how could they be addressed

While the multimodal encoder approach offers significant advantages in learning cross-modal relations, there are potential limitations and failure cases that need to be addressed. One limitation is the risk of overfitting to specific patterns in the training data, leading to reduced generalization performance on unseen samples. To mitigate this, techniques like regularization, data augmentation, or adversarial training could be employed to enhance the model's robustness. Additionally, the multimodal encoder may struggle with handling noisy or incomplete data, requiring preprocessing steps or data cleaning strategies to improve performance. Addressing these limitations through careful model design and data preprocessing can help mitigate failure cases and improve the overall effectiveness of the approach.

Could the pair selection method be further improved by incorporating additional criteria, such as speaker characteristics or facial attributes, to enhance the diversity and difficulty of the training samples

The pair selection method could be further improved by incorporating additional criteria related to speaker characteristics or facial attributes to enhance the diversity and difficulty of the training samples. By considering factors like age, gender, emotional expression, or speech characteristics, the pair selection process can create more challenging positive and negative pairs for training. This enhanced diversity can help the model learn more nuanced relationships between modalities and improve its ability to generalize to unseen data. Furthermore, incorporating facial attributes like facial hair, accessories, or facial expressions can introduce variability in the training samples, making the model more robust to different scenarios and improving its performance on real-world applications.