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
A Robust Approach Towards Distinguishing Natural and Computer Generated Images using Multi-Colorspace fused and Enriched Vision Transformer
Manjary P Gangan, Anoop Kadan, Lajish V L
Diffusion Based Augmentation for Captioning and Retrieval in Cultural Heritage
Dario Cioni, Lorenzo Berlincioni, Federico Becattini, Alberto del Bimbo
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