Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures (2403.02308v2)

Abstract: 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}.

Vision-RWKV: A Linear Complexity Vision Encoder for Efficient and Scalable Visual Perception

Introduction to Vision-RWKV

The recent emergence of Vision-RWKV (VRWKV) marks a significant milestone in the domain of computer vision. Derived from the RWKV model initially formulated for natural language processing tasks, VRWKV is meticulously engineered to cater to vision tasks while maintaining the efficiency and scalability characteristic of its predecessor. Notably, VRWKV stands out by offering a solution to the quadratic computational complexity challenge posed by conventional Vision Transformers (ViTs). This challenge has historically limited the application of ViTs in processing high-resolution images and handling long-sequence analysis. By introducing modifications such as quad-directional shift (Q-Shift) and transforming the attention mechanism to a bidirectional global attention schema, VRWKV substantially lowers spatial aggregation complexity.

Key Contributions and Findings

The core contributions of the VRWKV model can be summarized as follows:

• Introduction of VRWKV as a Low-Cost Alternative to ViT: VRWKV leverages a linear computational complexity approach to achieve efficient global information processing and handling of sparse inputs. By doing so, it eliminates the need for window-based attention in high-resolution image processing, proposing a scalable and more efficient methodology for vision tasks.
• Efficacy of Bidirectional Global Attention and Q-Shift: The paper details how the integration of bidirectional global attention and the novel Q-Shift method empowers VRWKV to maintain linear complexity in attention computation. Furthermore, several model stabilization techniques, like the adoption of layer scale and normalization methods, are proposed to ensure the model’s robust scalability.
• Comparative Analysis Against ViTs: The paper undertakes an exhaustive evaluation of VRWKV against ViTs across various benchmarks. Impressively, VRWKV not only matched but in some instances, surpassed the performance metrics of ViTs, especially in dense prediction tasks and high-resolution image classification. When trained on ImageNet-1K, VRWKV-T outperformed DeiT-T by 2.9 points, establishing VRWKV as a promising candidate for a wide range of vision tasks.

Implications and Future Directions

From a practical standpoint, VRWKV’s ability to deliver comparable or superior performance metrics at significantly reduced computational costs and memory consumption has profound implications. Especially noteworthy is its potential to democratize the deployment of state-of-the-art vision models in scenarios where computational resources are a constraint.

Looking ahead, the VRWKV model opens new avenues for exploration, particularly in tasks requiring high-resolution image processing and long-context analysis. There exists a promising potential for integrating VRWKV into real-world applications such as medical image analysis, satellite imagery interpretation, and beyond, where efficiency and accuracy are paramount.

Final Thoughts

In summary, VRWKV represents a pioneering stride towards circumventing the limitations posed by the computational inefficiency of traditional Vision Transformers. By embodying the principles of efficiency, scalability, and robust performance, VRWKV positions itself as a compelling alternative for advanced visual perception tasks. Future endeavors might explore extending the model's applicability to broader domains and further refining its architecture to achieve even greater efficiency and accuracy.