EmoMM: Benchmarking and Steering MLLM for Multimodal Emotion Recognition under Conflict and Missingness
Multimodal Emotion Recognition (MER) plays a pivotal role in understanding human interactions by analyzing various forms of data, including text, audio, and video. The advent of Multimodal Large Language Models (MLLM) has opened new avenues for MER, yet the intricacies of their decision-making processes, particularly in contexts of modality conflict and missing data, remain largely uncharted. Recent research has sought to address these gaps, culminating in the introduction of EmoMM, a comprehensive benchmark designed to evaluate and enhance MLLM performance in these challenging scenarios.
Introduction to EmoMM Benchmark
EmoMM, as detailed in the recent paper (arXiv:2605.01024v1), provides a systematic framework for examining MLLM behaviors in the face of modality conflict and missingness. The benchmark is unique in its structure, featuring three distinct subsets:
- Modality-aligned: Data where all modalities are present and aligned.
- Conflict: Scenarios where conflicting information is presented across modalities.
- Missing: Instances where one or more modalities are absent.
This categorization allows researchers to pinpoint specific areas where MLLMs may struggle and facilitates targeted improvements in model architecture and training methodologies.
Key Findings: Video Contribution Collapse
One of the significant discoveries in the EmoMM evaluation is the Video Contribution Collapse (VCC) phenomenon. This occurs when MLLMs marginalize video evidence during the decision-making process. The research indicates that this marginalization is often due to:
- High token redundancy within the video data.
- Inherent modality preferences that skew the model’s attention towards other modalities.
The implications of VCC are critical, as they suggest that MLLMs may not fully leverage the rich information contained in video data, potentially leading to suboptimal emotion recognition outcomes.
Proposed Solution: CHASE Mechanism
To combat the challenges posed by modality conflict and the VCC phenomenon, the researchers propose a novel solution: Conflict-aware Head-level Attention Steering (CHASE). This lightweight mechanism operates in the following manner:
- It detects instances of modality conflict during inference.
- It dynamically steers the attention of the model towards the most relevant modalities without necessitating retraining of the backbone model.
By implementing CHASE, the researchers have observed a consistent improvement in MER performance across various experimental settings. This enhancement underscores the potential for MLLMs to become more reliable in complex affective scenarios, ultimately leading to more accurate interpretations of human emotions.
Conclusion
The introduction of EmoMM represents a significant step forward in the quest to refine Multimodal Emotion Recognition systems. By systematically addressing the challenges posed by modality conflict and missingness, and through innovative solutions like CHASE, the research opens the door to more nuanced and effective emotional analysis in real-world applications. As MLLMs continue to evolve, frameworks like EmoMM will be essential for guiding future developments and ensuring that these models can effectively interpret the complexities of human emotion across diverse modalities.
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