Summary of Visualrwkv: Exploring Recurrent Neural Networks For Visual Language Models, by Haowen Hou and Peigen Zeng and Fei Ma and Fei Richard Yu
VisualRWKV: Exploring Recurrent Neural Networks for Visual Language Models
by Haowen Hou, Peigen Zeng, Fei Ma, Fei Richard Yu
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces VisualRWKV, a novel visual language model that combines the benefits of pre-trained RWKV language models with efficient linear Recurrent Neural Networks (RNNs) architectures. The proposed architecture incorporates data-dependent recurrence and sandwich prompts to enhance modeling capabilities, as well as a 2D image scanning mechanism for processing visual sequences. Experimental results demonstrate competitive performance compared to Transformer-based models like LLaVA-1.5 on various benchmarks, while showcasing a significant speed advantage (3.98 times) and reduced GPU memory usage (54% when reaching an inference length of 24K tokens). This work has the potential to facilitate further research and analysis in multimodal learning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a new way for computers to understand visual information, like pictures and videos. They combine two powerful tools: pre-trained language models that can process words and phrases, and linear RNNs that are good at processing sequences of data. The team adds some special techniques to make the model better at understanding images and videos. When they tested it on different tasks, their model performed as well as other popular models but was much faster and used less computer memory. This work could help people create new applications that combine language and visual information. |
Keywords
* Artificial intelligence * Inference * Language model * Transformer
