Cross Image Attention

Cross-image attention mechanisms are enhancing computer vision models by enabling the integration of information across multiple images. Current research focuses on leveraging this technique to improve tasks such as image segmentation (especially in medical imaging), object tracking, and depth estimation, often combining it with convolutional neural networks (CNNs) and transformers in hybrid architectures. This approach allows models to learn richer representations by exploiting both local and global contextual information, leading to improved accuracy and efficiency in various applications, including medical image analysis and autonomous driving.