Region Localization

Region localization focuses on identifying specific areas within images or 3D models that correspond to particular features or manipulations. Current research emphasizes improving the accuracy and robustness of localization, particularly in challenging scenarios like detecting inharmonious regions in edited images or localizing areas described by text prompts on 3D shapes. Methods often involve neural networks, including U-Net architectures and contrastive learning approaches, to analyze image features and generate localization maps. Advances in this field are crucial for applications ranging from detecting image forgeries to enhancing 3D modeling workflows and improving weakly supervised object localization.