Correlation-Decoupled Knowledge Distillation for Robust Multimodal Sentiment Analysis with Incomplete Modalities

Core Concepts

A correlation-decoupled knowledge distillation framework that effectively captures and transfers comprehensive cross-sample, cross-category, and cross-response correlations to reconstruct missing semantics and generate robust joint multimodal representations for multimodal sentiment analysis under uncertain missing modalities.

Abstract

The paper presents a Correlation-decoupled Knowledge Distillation (CorrKD) framework for the task of multimodal sentiment analysis (MSA) under uncertain missing modalities. The key contributions are: Sample-level Contrastive Distillation (SCD) mechanism: Captures holistic cross-sample correlations and transfers valuable supervision signals via sample-level contrastive learning. Category-guided Prototype Distillation (CPD) mechanism: Leverages category prototypes to transfer intra- and inter-category feature variations, delivering sentiment-relevant information and learning robust joint multimodal representations. Response-disentangled Consistency Distillation (RCD) strategy: Decouples heterogeneous responses and maximizes mutual information between homogeneous sub-responses to optimize sentiment decision boundaries and encourage distribution alignment. The proposed CorrKD framework significantly improves MSA performance under both uncertain missing-modality and complete-modality testing conditions on three multimodal benchmarks, demonstrating its strong robustness against modality missingness.

Stats

Multimodal data with language, audio, and visual modalities are used for sentiment analysis. Intra-modality missingness (missing frame-level features) and inter-modality missingness (missing entire modalities) are simulated to evaluate model robustness.

Quotes

"Correlations serve as the beacon through the fog of the missingness." "Traditional model outputs correct prediction when inputting the sample with complete modalities, but incorrectly predicts the sample with missing modalities."

Key Insights Distilled From

Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities

by Mingcheng Li... at arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16456.pdf

Deeper Inquiries

How can the proposed CorrKD framework be extended to other multimodal tasks beyond sentiment analysis, such as emotion recognition or video understanding

The CorrKD framework can be extended to other multimodal tasks beyond sentiment analysis by adapting its core components to suit the specific requirements of the new task. For tasks like emotion recognition or video understanding, the sample-level contrastive distillation mechanism can be modified to capture and transfer holistic correlations between different modalities to reconstruct missing information. Additionally, the category-guided prototype distillation mechanism can be adjusted to leverage category prototypes relevant to the new task to align feature distributions and generate joint representations. The response-disentangled consistency distillation strategy can also be tailored to optimize decision boundaries and encourage distribution alignment in the context of emotion recognition or video understanding. By customizing these components to the nuances of the new task, the CorrKD framework can effectively handle diverse multimodal challenges beyond sentiment analysis.

What are the potential limitations of the current CorrKD framework, and how can it be further improved to handle more complex and diverse missing modality scenarios

The current CorrKD framework may have limitations in handling extremely complex and diverse missing modality scenarios where the correlations between samples, categories, and responses are highly intricate. To address these limitations and further improve the framework, several enhancements can be considered. Firstly, incorporating more advanced contrastive learning techniques or leveraging self-supervised learning methods can enhance the sample-level contrastive distillation mechanism to capture even more nuanced correlations across samples. Secondly, refining the category-guided prototype distillation mechanism by incorporating more sophisticated category prototypes or exploring adaptive prototype generation techniques can improve the alignment of feature distributions in diverse missing modality scenarios. Lastly, enhancing the response-disentangled consistency distillation strategy by integrating more advanced mutual information maximization techniques or exploring novel ways to decouple heterogeneous responses can further optimize the robustness and effectiveness of the framework in handling complex multimodal challenges.

What are the theoretical insights behind the effectiveness of decoupling cross-sample, cross-category, and cross-response correlations for robust multimodal representation learning

The effectiveness of decoupling cross-sample, cross-category, and cross-response correlations for robust multimodal representation learning in the CorrKD framework is rooted in several theoretical insights. By decoupling these correlations, the framework can capture and transfer valuable supervision signals that encompass holistic information across samples, categories, and responses. This approach enables the model to reconstruct missing semantics more accurately and generate robust joint representations by aligning feature distributions and optimizing decision boundaries. Additionally, by maximizing mutual information between homogeneous responses and encouraging distribution alignment, the framework ensures that the student network learns informative knowledge from the teacher network, leading to improved performance in handling diverse missing modality scenarios. Overall, the theoretical underpinnings of decoupling these correlations contribute to the framework's effectiveness in multimodal representation learning.