V1 V2

Research on "V1 V2" encompasses diverse applications, primarily focusing on improving model robustness and performance across various domains. Current efforts involve developing and refining model architectures, such as EfficientNets and masked autoencoders, often incorporating biologically-inspired designs mimicking the primate visual cortex (V1) to enhance feature extraction and noise resilience. These advancements have implications for diverse fields, including medical image analysis (e.g., breast cancer detection), computer vision (e.g., improved pose estimation and video generation), and natural language processing (e.g., improved large language models for Japanese). The ultimate goal is to create more accurate, robust, and efficient models for a wide range of tasks.