Natural Image
Natural images, encompassing photographs and other visual data from the real world, are a central focus in computer vision research, aiming to enable machines to understand and interact with visual information as humans do. Current research emphasizes developing robust models, often leveraging architectures like Vision Transformers and diffusion models, to address challenges such as object detection, segmentation, and scene understanding in complex, diverse imagery. This work is crucial for advancing applications ranging from medical image analysis and autonomous navigation to improved image generation and quality assessment, ultimately bridging the gap between human and machine perception.
Papers
Testing predictions of representation cost theory with CNNs
Charles Godfrey, Elise Bishoff, Myles Mckay, Davis Brown, Grayson Jorgenson, Henry Kvinge, Eleanor Byler
From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution
Xiaoming Li, Chaofeng Chen, Xianhui Lin, Wangmeng Zuo, Lei Zhang