Multimodal Adaptation of Pre-Trained Unimodal Models for Dynamic Facial Expression Recognition in the Wild

Core Concepts

Achieving state-of-the-art performance on multimodal dynamic facial expression recognition in the wild by adapting pre-trained unimodal models without the need for large-scale multimodal pre-training.

Abstract

The paper proposes a method called MMA-DFER for multimodal dynamic facial expression recognition in the wild. The key contributions are:

Identification of three main challenges in adapting pre-trained unimodal models for multimodal DFER: intra-modality adaptation, cross-modal alignment, and temporal adaptation. The paper proposes solutions to address each of these challenges.

Intra-modality adaptation is achieved through progressive prompt learning, where learnable prompts are introduced at different depths of the model to bridge the domain gap between pre-training and downstream data.

Cross-modal alignment is addressed using Fusion Bottleneck blocks that compress and fuse features from the two modalities (audio and vision) in a low-dimensional latent space, and then expand them back to the original dimensionality with a gating mechanism.

Temporal adaptation is handled by a Multimodal Temporal Transformer that operates on the joint multimodal sequence to capture temporal dependencies.

Extensive experiments on two popular in-the-wild DFER benchmarks, DFEW and MAFW, show that the proposed MMA-DFER outperforms current state-of-the-art methods without requiring large-scale multimodal pre-training.

Stats

The DFEW dataset contains 16,000 audiovisual clips with 7 emotion classes.
The MAFW dataset contains 10,045 clips with 11 emotion classes.

Quotes

"Achieving robustness towards in-the-wild data in DFER is particularly important for real-world applications."
"We show that with appropriate adaptation, we can obtain beyond state-of-the-art results on two popular DFER benchmarks."

Key Insights Distilled From

MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild

by Kateryna Chu... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09010.pdf

Deeper Inquiries

How can the proposed approach be extended to handle more modalities beyond audio and vision, such as text or physiological signals, for a more comprehensive multimodal emotion recognition system?

The proposed approach can be extended to handle additional modalities by incorporating separate pre-trained encoders for each modality and then fusing the features at different stages of the model. For text modalities, a pre-trained language model can be used to extract textual features, while for physiological signals, specialized signal processing techniques can be employed to extract relevant features. These features can then be integrated using fusion techniques like Fusion Bottleneck blocks to combine the information from different modalities effectively. By adapting the fusion mechanisms and the architecture of the model, it is possible to create a more comprehensive multimodal system that can handle a diverse range of input modalities.

What are the potential limitations of the progressive prompt learning approach, and how could it be further improved to handle larger domain shifts between pre-training and downstream tasks?

One potential limitation of the progressive prompt learning approach is the complexity introduced by managing multiple sets of learnable prompts at different depths of the model. This can lead to increased computational overhead and potential difficulties in training convergence. To address this, the approach could be further improved by implementing adaptive mechanisms that dynamically adjust the prompt updates based on the model's learning progress. Additionally, incorporating regularization techniques to prevent overfitting and carefully tuning the hyperparameters related to prompt learning can help mitigate the challenges posed by larger domain shifts between pre-training and downstream tasks.

Given the focus on in-the-wild data, how could the proposed method be adapted to handle noisy, incomplete, or imbalanced data scenarios that are common in real-world deployments?

To adapt the proposed method to handle noisy, incomplete, or imbalanced data scenarios common in real-world deployments, several strategies can be employed.

Data Augmentation: Introduce data augmentation techniques to create variations in the training data, helping the model generalize better to noisy or incomplete inputs.
Regularization: Implement regularization methods such as dropout or batch normalization to prevent overfitting and improve model robustness.
Class Imbalance Handling: Utilize techniques like class weighting, oversampling, or undersampling to address imbalanced class distributions in the training data.
Ensemble Learning: Employ ensemble learning methods to combine predictions from multiple models trained on different subsets of the data, enhancing the model's performance and resilience to noise.
Transfer Learning: Leverage transfer learning by fine-tuning the model on a related task with more balanced data before training on the target dataset, allowing the model to learn more robust features.