Skip Transformer

Skip Transformers are a class of transformer networks designed to improve efficiency and performance by selectively processing information, skipping unnecessary computations. Current research focuses on applying Skip Transformers to diverse tasks, including 3D human pose estimation, human motion generation, and point cloud processing, often incorporating them within larger architectures like graph neural networks or variational autoencoders. These advancements aim to reduce computational complexity and memory requirements while maintaining or improving accuracy, leading to more efficient and scalable models for various applications in computer vision, animation, and natural language processing. The resulting improvements in efficiency and performance have significant implications for deploying these models on resource-constrained devices and for handling large-scale datasets.