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
Adapting Pre-Trained Vision Models for Novel Instance Detection and Segmentation
Yangxiao Lu, Jishnu Jaykumar P, Yunhui Guo, Nicholas Ruozzi, Yu Xiang
Towards a Generalist and Blind RGB-X Tracker
Yuedong Tan, Zongwei Wu, Yuqian Fu, Zhuyun Zhou, Guolei Sun, Chao Ma, Danda Pani Paudel, Luc Van Gool, Radu Timofte