Multimodal Emotion Understanding with Vision-Language Models: A Cross-Modal Attention Framework for Sentiment Analysis
Keywords:
multimodal sentiment analysis; vision-language models; cross-modal attention; emotion understanding; system architecture; fairness; deployment infrastructureAbstract
The emergence of vision-language models has enabled substantial progress in multimodal sentiment analysis, where emotional cues arise from the interplay between visual expressions and textual content. This paper presents a systems-oriented investigation of a cross-modal attention framework for emotion understanding using large-scale vision-language models. We examine the architectural design space, discussing the trade-offs inherent in dual-encoder versus fusion-based attention mechanisms, and the challenges of aligning heterogeneous modalities for reliable sentiment classification. Beyond algorithmic performance, we focus on infrastructure requirements for training and deploying these models, including distributed computing, model serving, and edge-optimized variants. Robustness to noisy or adversarial multimodal inputs, fairness across demographic groups in facial affect recognition, and privacy concerns in real-world deployment are analyzed in depth. We further articulate governance frameworks and policy guidelines necessary for responsible fielding of emotion AI systems. The paper contributes a comprehensive perspective that bridges technical architecture, operational sustainability, and socio-technical considerations, illustrating how cross-modal attention frameworks can be designed not only for accuracy but also for equity, transparency, and deployability at scale.
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