Long Range Transformer

Long-range Transformers aim to overcome the computational limitations of standard Transformers, which struggle with long input sequences due to quadratic complexity in self-attention. Current research focuses on developing efficient sparse attention mechanisms, hierarchical architectures, and conditional computation strategies to reduce this complexity while maintaining performance, with models like Longformer, Performer, and variations employing k-NN indexing emerging as prominent examples. These advancements are significantly impacting various fields, enabling improved performance in natural language processing tasks involving long documents, enhanced capabilities in computer vision applications like place recognition, and more accurate predictions in areas such as traffic flow forecasting.