Visual Encoder

Visual encoders are fundamental components of large vision-language models (LVLMs), tasked with extracting meaningful information from images to facilitate tasks like object recognition, scene understanding, and multimodal reasoning. Current research focuses on improving encoder efficiency (e.g., through token compression and adaptive token reduction), mitigating issues like object hallucination and enhancing robustness across diverse visual inputs (e.g., high-resolution images, multi-camera data, and various channel configurations). These advancements are crucial for deploying LVLMs in resource-constrained environments and expanding their applicability to real-world scenarios, such as autonomous driving and robotic manipulation. The development of more efficient and robust visual encoders is driving progress in various fields, including computer vision, natural language processing, and robotics.