Multimodal Video

Multimodal video analysis focuses on understanding video content by integrating information from multiple sources like visual, audio, and textual data, aiming to achieve more comprehensive and robust interpretations than unimodal approaches. Current research emphasizes developing sophisticated fusion models, including transformers and generative networks, to effectively combine these modalities, often incorporating techniques like cross-attention mechanisms and modality-specific encoders. This field is crucial for advancing applications such as driver monitoring, sentiment analysis, and media manipulation detection, while also contributing to fundamental research in areas like explainable AI and high-resolution video understanding.