Multiscale Transformer

Multiscale transformers are a class of neural network architectures designed to process data at multiple levels of granularity, improving performance on various sequence modeling tasks. Current research focuses on developing efficient multiscale architectures, such as slow-fast transformers and encoder-decoder models, to handle long sequences and incorporate diverse data modalities (e.g., video, audio, text). These advancements are significantly impacting fields like machine translation, video analysis, and long-sequence prediction by enabling more accurate and efficient processing of complex data, particularly where incomplete or high-dimensional information is present. The resulting models demonstrate improved performance over single-scale approaches across numerous benchmarks.