Vision Model
Vision models are artificial intelligence systems designed to interpret and understand visual information, aiming to replicate aspects of human visual perception and reasoning. Current research emphasizes improving efficiency and generalization across diverse tasks, focusing on architectures like Vision Transformers and Convolutional Neural Networks, often incorporating large language models for multimodal understanding and instruction following. This field is crucial for advancing various applications, from medical image analysis and robotic manipulation to enhancing accessibility and creative tools, with ongoing efforts to improve model robustness, explainability, and alignment with human perception.
Papers
Towards Reliable Assessments of Demographic Disparities in Multi-Label Image Classifiers
Melissa Hall, Bobbie Chern, Laura Gustafson, Denisse Ventura, Harshad Kulkarni, Candace Ross, Nicolas Usunier
Towards Efficient Visual Adaption via Structural Re-parameterization
Gen Luo, Minglang Huang, Yiyi Zhou, Xiaoshuai Sun, Guannan Jiang, Zhiyu Wang, Rongrong Ji