Token Merging

Token merging is a technique used to enhance the efficiency of transformer-based models, primarily by reducing the computational burden associated with processing a large number of input tokens. Current research focuses on developing efficient merging algorithms, often integrated into Vision Transformers (ViTs) and other architectures like Mamba models, with a strong emphasis on minimizing accuracy loss while maximizing speed improvements across various modalities (image, video, audio). This research is significant because it addresses the scalability limitations of transformers, enabling faster inference and reduced memory consumption for applications ranging from image classification and generation to video analysis and speech recognition.