Visual Language Models (VLMs) have rapidly progressed with the recent success
of large language models. However, there have been few attempts to incorporate
efficient linear Recurrent Neural Networks (RNNs) architectures into VLMs. In
this study, we introduce VisualRWKV, the first application of a linear RNN
model to multimodal learning tasks, leveraging the pre-trained RWKV language
model. We propose a data-dependent recurrence and sandwich prompts to enhance
our modeling capabilities, along with a 2D image scanning mechanism to enrich
the processing of visual sequences. Extensive experiments demonstrate that
VisualRWKV achieves competitive performance compared to Transformer-based
models like LLaVA-1.5 on various benchmarks. To facilitate further research and
analysis, we have made the checkpoints and the associated code publicly
accessible at the following GitHub repository:
\href{https://github.com/howard-hou/VisualRWKV}{this https URL}.

通过引入一种线性循环神经网络模型到多模态学习任务中，该研究提出了 VisualRWKV，它是首个应用线性 RNN 模型到视觉语言模型中的尝试，并通过数据相关的循环和提示增强建模能力，以及 2D 图像扫描机制来丰富对视觉序列的处理，实验证明 VisualRWKV 在各项基准测试中与基于 Transformer 的模型（如 LLaVA-1.5）相比具有竞争性能。