# VisualRWKV-HD and UHD: Advancing High-Resolution Processing for Visual Language Models

arXiv: https://arxiv.org/abs/2410.11665 · HTML: https://mlsanity.com/paper/2410.11665/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 15 Oct 2024.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 11 of 11)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [Towards VQA Models That Can Read](https://mlsanity.com/paper/1904.08920/) (2019)
- [MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2306.13394/) (2023)
- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [DocVQA: A Dataset for VQA on Document Images](https://mlsanity.com/paper/2007.00398/) (2020)
- [RWKV: Reinventing RNNs for the Transformer Era](https://mlsanity.com/paper/2305.13048/) (2023)
- [VL-Mamba: Exploring State Space Models for Multimodal Learning](https://mlsanity.com/paper/2403.13600/) (2024)
- [Learnable pooling with Context Gating for video classification](https://mlsanity.com/paper/1706.06905/) (2017)
- [VisualRWKV: Exploring Recurrent Neural Networks for Visual Language Models](https://mlsanity.com/paper/2406.13362/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Survey of RWKV](https://mlsanity.com/paper/2412.14847/) (2024)

## Abstract (arXiv)

Accurately understanding complex visual information is crucial for visual language models (VLMs). Enhancing image resolution can improve visual perception capabilities, not only reducing hallucinations but also boosting performance in tasks that demand high resolution, such as text-rich or document analysis. In this paper, we present VisualRWKV-HD and VisualRWKV-UHD, two advancements in the VisualRWKV model family, specifically designed to process high-resolution visual inputs. For VisualRWKV-HD, we developed a lossless downsampling method to effectively integrate a high-resolution vision encoder with low-resolution encoders, without extending the input sequence length. For the VisualRWKV-UHD model, we enhanced image representation by dividing the image into four segments, which are then recombined with the original image. This technique allows the model to incorporate both high-resolution and low-resolution features, effectively balancing coarse and fine-grained information. As a result, the model supports resolutions up to 4096 x 4096 pixels, offering a more detailed and comprehensive visual processing capability. Both VisualRWKV-HD and VisualRWKV-UHD not only achieve strong results on VLM benchmarks but also show marked improvements in performance for text-rich tasks.