Hierarchical Contrastive Masked Autoencoder for Self-Supervised Audio-Visual Emotion Recognition

Centrala begrepp

HiCMAE, a novel self-supervised framework, leverages large-scale self-supervised pre-training on unlabeled audio-visual data to promote the advancement of audio-visual emotion recognition.

Sammanfattning

The content presents HiCMAE, a novel self-supervised framework for audio-visual emotion recognition (AVER). HiCMAE adopts two primary forms of self-supervision: masked audio-visual reconstruction and contrastive learning. Unlike previous methods that focus exclusively on top-layer representations, HiCMAE introduces a three-pronged strategy to foster hierarchical audio-visual feature learning:

Hierarchical skip connections between the encoder and decoder to encourage intermediate layers to learn more meaningful representations and aid the decoder in accomplishing the task of masked audio-visual reconstruction.

Hierarchical cross-modal contrastive learning on intermediate representations to narrow the audio-visual modality gap progressively and facilitate subsequent cross-modal fusion.

Hierarchical feature fusion during downstream fine-tuning to comprehensively integrate multi-level features from different encoder layers.

Extensive experiments on 9 datasets covering both categorical and dimensional AVER tasks demonstrate that HiCMAE significantly outperforms state-of-the-art supervised and self-supervised audio-visual methods, indicating its effectiveness as a powerful audio-visual emotion representation learner.

Statistik

The MAFW dataset contains 9,172 video clips annotated with 11 common emotions.
The DFEW dataset contains 11,697 single-labeled video clips annotated with 7 basic emotions.
The MER-MULTI dataset contains 3,784 video clips annotated with 6 emotions.

Citat

"The question is not whether intelligent machines can have any emotions, but whether machines can be intelligent without any emotions." - Marvin Minsky

Viktiga insikter från

HiCMAE

by Licai Sun,Zh... på arxiv.org 04-02-2024

https://arxiv.org/pdf/2401.05698.pdf

Djupare frågor

How can the self-supervised pre-training of HiCMAE be further improved to better capture the nuanced and complex nature of human emotions

To further enhance the self-supervised pre-training of HiCMAE for capturing the nuanced and complex nature of human emotions, several improvements can be considered:

Incorporating Multimodal Context: Integrate additional modalities such as text or physiological signals to provide a more comprehensive understanding of emotions. By including diverse sources of information, the model can learn richer representations that encompass various aspects of emotional expression.

Fine-Grained Emotion Labeling: Utilize more detailed emotion labels that capture subtle variations in emotional states. Fine-grained annotations can help the model differentiate between closely related emotions and improve its ability to recognize complex emotional expressions.

Data Augmentation Techniques: Implement advanced data augmentation methods specific to audio-visual data, such as temporal jittering, color transformations, or audio perturbations. Augmenting the training data with diverse variations can help the model generalize better to unseen emotional cues.

Transfer Learning from Pre-trained Models: Utilize pre-trained models from related tasks such as sentiment analysis or facial expression recognition to initialize the HiCMAE framework. Transfer learning can provide a head start by leveraging knowledge learned from large-scale datasets.

Adversarial Training: Incorporate adversarial training techniques to encourage the model to learn robust and discriminative features for emotion recognition. Adversarial training can help the model capture subtle emotional cues while being invariant to irrelevant variations in the data.

What are the potential limitations of the current HiCMAE framework, and how could it be extended to handle more challenging real-world scenarios, such as multi-person interactions or dynamic emotional expressions

The current HiCMAE framework may have limitations when applied to more challenging real-world scenarios, such as multi-person interactions or dynamic emotional expressions. To address these limitations and extend the framework for handling complex scenarios, the following strategies can be considered:

Multi-Person Interaction Modeling: Extend HiCMAE to incorporate mechanisms for modeling interactions between multiple individuals in a scene. This can involve attention mechanisms that dynamically focus on different individuals or hierarchical structures that capture group dynamics.

Temporal Modeling: Enhance the framework with temporal modeling capabilities to capture the dynamic nature of emotional expressions over time. This can involve recurrent neural networks or transformer-based architectures that can effectively model temporal dependencies in audio-visual data.

Contextual Information Integration: Integrate contextual information from the environment or social cues to better understand the context in which emotions are expressed. Contextual information can provide valuable insights into the underlying reasons for emotional expressions and improve recognition accuracy.

Adaptation to Uncontrolled Environments: Modify the framework to adapt to uncontrolled environments with varying lighting conditions, background noise, or occlusions. Robust feature extraction methods and data augmentation techniques can help the model generalize well in diverse settings.

Real-Time Processing: Optimize the framework for real-time processing to handle dynamic emotional expressions efficiently. This can involve lightweight model architectures, efficient inference strategies, and parallel processing techniques for faster computation.

Given the success of HiCMAE in audio-visual emotion recognition, how could the hierarchical feature learning and fusion strategies be applied to other multimodal tasks beyond emotion recognition, such as human-robot interaction or affective computing in healthcare

The hierarchical feature learning and fusion strategies employed in HiCMAE can be applied to various multimodal tasks beyond emotion recognition, such as human-robot interaction or affective computing in healthcare. Here are some ways to adapt these strategies to other domains:

Human-Robot Interaction: Utilize hierarchical feature fusion to integrate information from different sensors (e.g., cameras, microphones, touch sensors) in a robot to understand human emotions better. The model can learn to adapt its behavior based on the emotional cues of the users.

Affective Computing in Healthcare: Apply hierarchical feature learning to analyze multimodal data (e.g., patient's facial expressions, voice, and physiological signals) for emotion recognition in healthcare settings. The model can assist in monitoring patient emotions and providing personalized care.

Multimodal Sentiment Analysis: Extend the hierarchical feature learning approach to multimodal sentiment analysis tasks, where text, audio, and visual data are combined to infer sentiment. The model can learn to extract sentiment-related features from diverse modalities for more accurate analysis.

Behavioral Analysis: Use hierarchical feature learning to analyze complex human behaviors in scenarios like educational settings or customer interactions. By fusing information from multiple modalities, the model can capture nuanced behavioral patterns and provide valuable insights.

Social Signal Processing: Apply hierarchical feature fusion techniques to social signal processing tasks, where audio, video, and physiological signals are analyzed to understand social interactions. The model can learn to extract meaningful features for detecting social cues and dynamics.