Visual Language Task

Visual language tasks focus on enabling AI models to understand and reason using both visual and textual information, aiming to bridge the gap between computer vision and natural language processing. Current research emphasizes improving the robustness and efficiency of multimodal large language models (MLLMs) through techniques like multi-instance learning, efficient transformer architectures (e.g., Mamba), and novel training strategies such as contrastive learning and inner monologue optimization. These advancements are significant for applications ranging from robotic surgery and image segmentation to more general visual question answering and commonsense reasoning, ultimately contributing to more sophisticated and versatile AI systems.