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Beyond bigger models: How efficient multimodal AI is redefining the future of intelligence

Multimodal large language models have shown powerful abilities to understand and reason across text and images, but their ...
Scientific Research Publishing

Multimodal Digital Phenotyping for Bipolar Disorder: Robust Mood-State Classification and Early Relapse Risk Monitoring ()

Bipolar Disorder, Digital Phenotyping, Multimodal Learning, Face/Voice/Phone, Mood Classification, Relapse Prediction, T-SNE, Ablation Share and Cite: de Filippis, R. and Al Foysal, A. (2025) ...
IEEE

3D Convolutional Neural Networks for Brain Tumor Analysis in Multimodal MRI: A Systematic Review

Abstract: Convolutional neural networks (CNNs) have emerged as a preferred approach for medical image analysis. The dimensionality of images is a principal factor in CNN models, as they are designed ...
Microsoft

Discourse Segmentation of Multi-Party Conversation

We present a domain-independent topic segmentation algorithm for multi-party speech. Our feature-based algorithm combines knowledge about content using a text-based algorithm as a feature and about ...
IEEE

Multimodal Aspect-Based Sentiment Analysis With Plugin-Enhanced Large Language Models

Abstract: Multimodal aspect-based sentiment analysis (MABSA) is a challenging task that predicts sentiment polarity for specific aspect terms based on inputs across modalities. Existing approaches ...