Vision Language Connector

Vision-language connectors are crucial components of multimodal large language models (MLLMs), bridging pre-trained visual encoders and LLMs to enable effective multimodal understanding. Current research focuses on improving the efficiency and effectiveness of these connectors, exploring architectures like "dense connectors" that leverage multi-layer visual features and methods that utilize "visual anchors" for efficient information aggregation. These advancements aim to enhance MLLM performance across various tasks, including image and video understanding, while minimizing computational costs, ultimately leading to more efficient and powerful multimodal AI systems.