Cross Modal Transformer

Cross-modal transformers are neural network architectures designed to effectively integrate and process information from multiple data modalities, such as images, text, and audio, to improve performance on various tasks. Current research focuses on developing efficient transformer-based models, including those employing cross-attention mechanisms and novel fusion strategies, to address challenges like computational cost and modality misalignment. These advancements are significantly impacting fields ranging from medical image analysis and autonomous driving to speech recognition and video understanding, enabling more robust and accurate solutions in these domains. The development of benchmark datasets and readily available codebases further accelerates progress and facilitates wider adoption.