Unified Visual

Unified visual representation aims to create a single, consistent way to encode both images and videos for use in large language models, improving multimodal understanding and cross-task learning. Current research focuses on developing model architectures that learn unified visual representations from diverse datasets, often employing techniques like multi-perspective view aggregation and dynamic visual tokenization to handle variations in camera parameters and data modalities. This work is significant because it enhances the performance of vision-language models across various downstream tasks, including robotic manipulation, image and video question answering, and medical image analysis, leading to more robust and versatile AI systems.