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
Human-M3: A Multi-view Multi-modal Dataset for 3D Human Pose Estimation in Outdoor Scenes
Bohao Fan, Siqi Wang, Wenxuan Guo, Wenzhao Zheng, Jianjiang Feng, Jie Zhou
Visual attention information can be traced on cortical response but not on the retina: evidence from electrophysiological mouse data using natural images as stimuli
Nikos Melanitis, Konstantina Nikita
Video Pretraining Advances 3D Deep Learning on Chest CT Tasks
Alexander Ke, Shih-Cheng Huang, Chloe P O'Connell, Michal Klimont, Serena Yeung, Pranav Rajpurkar
Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images
Roberto Amoroso, Davide Morelli, Marcella Cornia, Lorenzo Baraldi, Alberto Del Bimbo, Rita Cucchiara