Vision-RWKV (VRWKV) adapts the RWKV model from NLP for visual tasks, demonstrating significant advantages in processing high-resolution images. It delivers enhanced performance over Vision Transformer (ViT) with faster speeds and lower memory usage. See the results

• Vision-RWKV efficiently handles sparse inputs and boasts robust global processing.
• Shows substantial improvement in speed and memory when processing high-resolution images.
• Can process images without requiring windowing operations, unlike window-based models.
• Offers potential as a more efficient alternative for visual perception tasks.

This model showcases a leap towards addressing the computational complexity of high-resolution image processing, making it an influential advancement for the computer vision community.