Deformable Transformer

Deformable transformers are a class of deep learning models that improve upon traditional transformers by using sparse attention mechanisms, reducing computational complexity while maintaining strong performance. Current research focuses on applying deformable transformers to various tasks, including image and video object detection and segmentation, 3D reconstruction, audio event detection, and medical image analysis, often incorporating them into encoder-decoder architectures or hybrid models combining them with convolutional neural networks. This approach offers significant advantages in efficiency and accuracy across diverse applications, leading to improvements in areas such as medical diagnosis, autonomous systems, and multimedia processing.