Vision Graph

Vision graphs represent images as graph structures, enabling graph neural networks (GNNs) to process visual data, aiming to improve efficiency and accuracy in computer vision tasks. Current research focuses on developing efficient graph construction methods, such as dynamic axial graph construction and windowed approaches, to mitigate the computational complexity associated with large images, and on hybrid CNN-GNN architectures that leverage the strengths of both approaches. These advancements are leading to improved performance in image classification, object detection, and other vision tasks, particularly on mobile devices, while also addressing challenges in video domain adaptation and multimodal graph reasoning.