Image Detection

Image detection research focuses on automatically identifying and classifying objects or features within images, encompassing tasks like object detection, forgery detection, and salient object detection. Current research emphasizes developing robust and efficient models, often employing deep learning architectures such as convolutional neural networks (CNNs), graph convolutional networks (GCNs), and transformers, along with techniques like multi-scale feature extraction and attention mechanisms to improve accuracy and generalization. These advancements have significant implications for various applications, including industrial automation (defect detection), media forensics (deepfake detection), and enhancing the trustworthiness of AI systems by quantifying prediction uncertainty.