Synthetic Multi Modal Dataset

Synthetic multimodal datasets are artificially generated datasets combining multiple data modalities (e.g., images, LiDAR, text, audio) to address the limitations of real-world data in training complex machine learning models, particularly in areas like autonomous driving and medical imaging. Current research focuses on generating realistic and diverse datasets using various techniques, including physically accurate simulations, diffusion models, and domain randomization, often coupled with novel annotation methods to improve data quality and efficiency. These datasets are crucial for advancing research in areas requiring large, high-quality training data, enabling the development of more robust and accurate models for applications ranging from autonomous vehicle perception to medical image analysis and multimodal language understanding.