  Transformers have revolutionized computer vision and natural language
processing, but their high computational complexity limits their application in
high-resolution image processing and long-context analysis. This paper
introduces Vision-RWKV (VRWKV), a model adapted from the RWKV model used in the
NLP field with necessary modifications for vision tasks. Similar to the Vision
Transformer (ViT), our model is designed to efficiently handle sparse inputs
and demonstrate robust global processing capabilities, while also scaling up
effectively, accommodating both large-scale parameters and extensive datasets.
Its distinctive advantage lies in its reduced spatial aggregation complexity,
which renders it exceptionally adept at processing high-resolution images
seamlessly, eliminating the necessity for windowing operations. Our evaluations
demonstrate that VRWKV surpasses ViT's performance in image classification and
has significantly faster speeds and lower memory usage processing
high-resolution inputs. In dense prediction tasks, it outperforms window-based
models, maintaining comparable speeds. These results highlight VRWKV's
potential as a more efficient alternative for visual perception tasks. Code is
released at \url{https://github.com/OpenGVLab/Vision-RWKV}.
