Global Semantics

Global semantics in computer vision and natural language processing focuses on integrating contextual information across entire inputs (images, videos, text) to improve model understanding and performance. Current research emphasizes incorporating global context into existing architectures like transformers and convolutional neural networks, often through novel modules that fuse global and local features or explicitly model semantic relationships. This work is improving the accuracy and efficiency of tasks ranging from object detection and change detection in remote sensing to video question answering and medical image analysis, leading to more robust and interpretable AI systems.