  Transformers are widely used as generic backbones in computer vision, despite
initially introduced for natural language processing. Recently, the Long
Short-Term Memory (LSTM) has been extended to a scalable and performant
architecture - the xLSTM - which overcomes long-standing LSTM limitations via
exponential gating and parallelizable matrix memory structure. In this report,
we introduce Vision-LSTM (ViL), an adaption of the xLSTM building blocks to
computer vision. ViL comprises a stack of xLSTM blocks where odd blocks process
the sequence of patch tokens from top to bottom while even blocks go from
bottom to top. Experiments show that ViL holds promise to be further deployed
as new generic backbone for computer vision architectures.
