Image Semantics

Image semantics focuses on understanding the meaning and relationships within images, aiming to bridge the gap between raw visual data and high-level concepts. Current research emphasizes learning hierarchical representations of image content, often employing convolutional neural networks, transformers, and recurrent networks to capture both global and local features, as well as the spatial arrangement of image components. This work is crucial for improving various applications, including image quality assessment, object detection, and vision-and-language tasks, by enabling more robust and accurate interpretation of visual information. The development of effective image semantic models is driving progress in fields ranging from computer vision to graphic design and autonomous navigation.