Knowledge Injected U Transformer

Knowledge-injected U-Transformers represent a significant advancement in leveraging external knowledge to enhance the performance of transformer-based models across diverse applications. Current research focuses on integrating various forms of prior knowledge, such as anatomical structures, motion trajectories, or probabilistic knowledge graphs, into the transformer architecture, often through specialized attention mechanisms or knowledge distillation techniques. This approach improves model accuracy and interpretability in tasks ranging from 3D human pose estimation and binary code embedding to radiology report generation and multimodal emotion recognition. The resulting models demonstrate superior performance compared to traditional transformer architectures, highlighting the potential of knowledge integration for building more robust and effective AI systems.