Recent Transformer Based

Recent research focuses on leveraging transformer architectures for various computer vision tasks, moving beyond their initial success in natural language processing. Current efforts concentrate on improving transformer-based models by addressing limitations such as capturing local dependencies and handling multi-scale features, often through hybrid approaches combining transformers with other network types like graph convolutional networks. These advancements are significantly impacting fields like medical image analysis and remote sensing, achieving state-of-the-art results in tasks such as 3D pose estimation, hand mesh recovery, and action detection, while also improving efficiency. The resulting improvements in accuracy and computational efficiency have broad implications for numerous applications.