Feast Your Eyes: Mixture-of-Resolution Adaptation for Multimodal Large Language Models

## Abstract

Abstract Despite remarkable progress, existing multimodal large language models (MLLMs) are still inferior in granular visual recognition. Contrary to previous works, we study this problem from the perspective of image resolution, and reveal that a combination of low- and high-resolution visual features can effectively mitigate this shortcoming. Based on this observation, we propose a novel and efficient method for MLLMs, termed Mixture-of-Resolution Adaptation (MRA). In particular, MRA adopts two visual pathways for images with different resolutions, where high-resolution visual information is embedded into the low-resolution pathway via the novel mixture-of-resolution adapters (MR-Adapters). This design also greatly reduces the input sequence length of MLLMs. To validate MRA, we apply it to a recent MLLM called LLaVA, and term the new model LLaVA-HR . We conduct extensive experiments on 11 vision-language (VL) tasks, which show that LLaVA-HR outperforms existing MLLMs on 8 VL tasks, e.g., +9.4% on TextVQA. More importantly, both training and inference of LLaVA-HR remain efficient with MRA, e.g., 20 training hours and 3 × \times × inference speed than LLaVA-1.5. Source codes are released at: https://github.com/luogen1996/LLaVA-HR .

## 1 Introduction

Driven by the remarkable success of large language models (LLMs) (Touvron et al., ; Chen et al., ), research on multi-modal large language models (MLLMs) also receives an influx of interest in the machine learning community (Liu et al., ; Luo et al., ; Alayrac et al., ; Chen et al., , ). Numerous efforts have been recently devoted to extending LLMs to more modalities, achieving breakthroughs on various vision-language tasks (Goyal et al., ; Singh et al., ; Hudson & Manning, ). Despite advances, existing MLLMs still fall short of granular visual recognition. For instance, the powerful GPT4-V also suffers from hallucinations when identifying small and occluded objects (Tong et al., ). This shortcoming inevitably limits the practical use of MLLMs.

Figure: Figure 1: Zero-shot performance and inference speed of LLaVA-HR and existing MLLMs on TextVQA. Existing MLLMs often fall short of fine-grained VL tasks like TextVQA. Increasing image resolution is an effective yet expensive solution. With the proposed MRA, our LLaVA-HR can efficiently adopt high-resolution images to boost performance.
Refer to caption: x1.png

Figure: Figure 2: Comparison between existing MLLMs and LLaVA-HR. Due to high computation complexity, existing MLLMs (Liu et al., ; Li et al., ) often use input images of low-resolution, which are insufficient for granular visual reasoning. With our mixture-of-resolution adaptation, the proposed LLaVA-HR can increase the image resolution up to 1,536 $\times$ 1,536 with limited additional costs.
Refer to caption: x2.png

To compensate for this shortcoming, practitioners often resort to scaling up model size and increasing per-training data size (Alayrac et al., ; Li et al., ; Bai et al., ). For instance, InstructBLIP (Dai et al., ) adopts over 129M image-text pairs for vision-language (VL) alignments, and shows that a larger visual encoder is beneficial for MLLMs. Motivated by this, Qwen-VL (Bai et al., ) further increases the parameters of visual encoder to 1.9 billion and uses 1.5 billion pre-training data. Despite progress, this paradigm is prohibitively expensive, which often
consumes about thousands of GPU hours.

Orthogonal to these works, we study the visual shortcoming of MLLMs from the perspective of input image resolutions. As revealed in previous VL research (Jiang et al., ; Tong et al., ; Luo et al., ), increasing the resolution of input images is a straightforward solution to improve visual recognition, which becomes more important for MLLMs that involve visual chain-of-thought (Rose et al., ). As shown in Fig. 1, increasing the resolution of LLaVA-1.5 (Liu et al., ) from 384 $\times$ 384 to 672 $\times$ 672 can bring obvious performance gains (+4.6%) on TextVQA (Singh et al., ). However, the use of high-resolution images will greatly exacerbate the already high computational cost of MLLMs. For instance, $448\times 448$ resolution will increase the computation complexity of LLaVA by about 1.4 times compared with the default $336\times 336$. In addition, due to the complex structure of MLLMs, the training will become unstable as the resolution is greatly increased, *e.g.*, a sharp drop at $1,022\times 1,022$ resolution, as shown in Fig. 1. We assume that the length of visual sequences greatly exceeds the pre-trained context length, leading to training instability.

In this paper, we propose a novel and efficient method for the high-resolution image adaptation of MLLMs, namely mixture-of-resolution adaptation (MRA). As shown in Fig. 1, MRA adopts an innovative dual visual pathway design to process the input images of high- and low-resolutions simultaneously.
Specifically, one pathway aims to encode global information of low-resolution images, while the other one serves to capture fine-grained semantics from high-resolution images. Meanwhile, these two pathways are closely interacted via the novel mixture-of-resolution adapters (MR-Adapters), which embeds the high-resolution visual information into the low-resolution modeling. In this way, we can use a much fewer number of visual tokens to represent the input images from macro- to micro-views. With the careful design of dual-pathway structure, MRA can easily increase the image resolution up to 1,536 $\times$ 1,536 pixels while maintaining high efficiency.

To validate MRA, we apply it to a recent MLLLM called LLaVA (Liu et al., , ), and term the new model as LLaVA-HR. We conduct extensive experiments on 11 vision-language (VL) tasks, including common VL tasks like VQA2.0 (Goyal et al., ) and emerging benchmarks such as POPE (Li et al., ). Experimental results show that LLaVA-HR outperforms existing MLLMs on 8 of 11 VL tasks, *e.g.,* +9.6% over LLaVA-1.5 on TextVQA. More importantly, the training and inference of LLaVA-HR are cost-effective. The pre-training and instruction tuning of LLaVA-HR (7B, 1,024 $\times$ 1,024) only take a total of 20.7 hours on 8 A800 GPUs, which is hundreds of times cheaper than InstructBLIP (Dai et al., ) and Qwen-VL (Bai et al., ). With the same resolution, its inference speed is 3 times faster than LLaVA-1.5 (Liu et al., ).

In summary, our contributions are three folds:

- •
We reveal the significance of image resolution for MLLMs and propose a novel and efficient adaptation scheme, termed *mixture-of-resolution adaption* (MRA), which adopts a novel dual visual pathway design to obtain the benefits of high-resolution visual information while keeping training and inference efficient.
- •
We propose a novel mixture-of-resolution adapter (MR-Adapter) for MRA, which can embed the high-resolution information into the low-resolution visual pathway to improve visual descriptive power.
- •
Based on MRA, we propose a powerful MLLM, coined LLaVA-HR, which outperforms existing MLLMs on 8 of 11 VL tasks and spends much cheaper training expenditure than most MLLMs.

## 2 Related Work

### 2.1 Multimodal Large Language Models

Driven by the great successes of large language models (LLMs) (Gilardi et al., ; Touvron et al., ; Chen et al., ), growing interest has been aroused in building end-to-end multimodal large language models (MLLMs) (Liu et al., ; Zhu et al., ; Luo et al., ; Fuyu-8B, ; Peng et al., ; Liu et al., ). In particular, most existing MLLMs adopt a modular structure (Luo et al., ; Liu et al., ), which utilizes an intermediate network to project the visual features into the word embedding space of the LLM. Then, the LLM is used to accomplish various VL tasks in an autoregressive manner. Based on the modular structure, existing MLLMs can be distinguished by the designs of the intermediate network. Popular MLLMs represented by LLaVA (Liu et al., ) often adopt a linear projection layer or an MLP layer to connect the visual encoder and the LLM (Liu et al., , ; Chen et al., , ; Peng et al., ). The other works employ sampler-based modules to bridge the gap between the visual encoder and the LLM (Bai et al., ; Alayrac et al., ; Li et al., ; Dai et al., ). These sampler-based modules can effectively reduce the number of visual tokens, but often requires a large-scale pre-training to achieve a promising performance (Bai et al., ; Li et al., ). Despite the effectiveness, most existing MLLMs still adopt a low visual resolution, *e.g.,* 336 $\times$ 336, which greatly limits their performance in fine-grained tasks.

### 2.2 Visual Representations for MLLMs

The pursuit of better visual representations has been a popular research trend in the VL community (Lu et al., ; Jiang et al., ; Radford et al., ; Ren et al., ). Early endeavors mainly explore the object-level features for VL models (Lu et al., ; Zhang et al., ). Driven by the large-scale image-text pre-training, grid features from CLIP (Radford et al., ) have demonstrated the great efficiency and generalization in MLLMs (Liu et al., ; Chen et al., ; Alayrac et al., ). Based on grid features, existing researchers mainly improve visual representations by scaling up the visual encoder. For example, PaLI (Chen et al., ) increases the parameters of visual encoder to 3 billions and shows the significant performance boost of MLLMs. In contrast to these works, we improve the visual representations for MLLMs from the perspective of image resolution, and propose a novel and efficient solution, namely mixture-of-resolution adaptation.

Figure: Figure 3: Illustration of Mixture-of-Resolution Adaptation (MRA) and its deployment on LLaVA-HR. MRA employs dual visual pathways to process high-resolution and low-resolution images, respectively. High-resolution information is embeded into the fast pathway via a novel mixture-of-resolution adapter (MR-Adapter).
Refer to caption: x3.png

## 3 Preliminary

We first recap the structure of multimodal large language models (MLLMs), which consists of an image encoder $\mathcal{F_{I}(\cdot)}$, an intermediate network $\mathcal{F_{P}(\cdot)}$ and an LLM $\mathcal{F_{L}(\cdot)}$.

In particular, given an input image $I\in\mathbb{R}^{H\times W\times 3}$ and a textual instruction $T\in\mathbb{R}^{L}$, the visual tokens $\textbf{F}_{v}\in\mathbb{R}^{(h\times w)\times d}$ are obtained via the image encoder, and the text tokens $f_{t}\in\mathbb{R}^{l\times d}$ are represented by the corresponding word embeddings. Based on the visual and textual tokens, the LLM will decode the target word step by step, formulated as

$$ $\displaystyle p_{t}=\prod_{s=1}^{S+1}\mathcal{F_{L}}(R_{s}|\mathcal{F_{P}}( \textbf{F}_{v}),f_{t},R_{0:s-1}).$ (1) $$

Here, $p_{t}\in\mathbb{R}^{m}$ denotes the probabilities of the predicted word and $m$ is the size of word vocabulary.

In some MLLMs (Liu et al., , ), $\mathcal{F_{P}}(\cdot)$ is often a stack of simple linear layers, which are used to directly project the visual tokens onto the semantic space of LLMs. Although simple and effective, this strategy inevitably leads to a longer visual sequence as the resolution increases, *e.g.,* 5,329 tokens for 1,022 $\times$ 1,022 resolution in LLaVA-1.5. In practice, processing such a large number of tokens is computationally expensive in MLLMs.
To further reduce the number of visual tokens, recent advances adopt the sampler-based module for $\mathcal{F_{P}}(\cdot)$ , *e.g.,* QFormer (Li et al., ), which aggregates visual features into several tokens that LLM can directly handle. Nevertheless, these methods often require large-scale pre-training to achieve VL alignments (Bai et al., ; Li et al., ).

Based on the above analyses, we conclude that the main difficulty of high-resolution image adaptation lies in the rapidly growing visual sequence. This issue motivates us to further explore how to efficiently encode richer visual information with fewer visual tokens.

## 4 Mixture-of-Resolution Adaptation

### 4.1 Overview

To address the above issues, we propose a novel and efficient method for MLLMs, termed mixture-of-resolution adaptation (MRA), of which structure is depicted in Fig. 3. The core idea of MRA is to embed high-resolution information into the low-resolution one via a dual pathway design. In this case, MRA can keep a smaller number of visual tokens while encoding richer visual information.

Particularly, given the input images of two resolutions $I_{l}\in\mathbb{R}^{H_{l}\times W_{l}\times 3}$ and $I_{h}\in\mathbb{R}^{H_{h}\times W_{h}\times 3}$, the process of MRA can be formulated as

$$ $\displaystyle\textbf{F}_{v}=\mathcal{F}_{\mathcal{I}_{l}}(I_{l},\mathcal{F_{A} }(\textbf{F}_{vh}))+\textbf{F}_{vh},$ (2) $\displaystyle\textbf{F}_{vh}=\mathcal{F}_{\mathcal{I}_{h}}(I_{h}).$ $$

Here, $\textbf{F}_{vh}\in\mathbb{R}^{h_{h}\times w_{h}\times d_{h}}$ and $\textbf{F}_{v}\in\mathbb{R}^{h\times w\times d}$ denote the high-resolution features and the final visual features, respectively.
And $\mathcal{F}_{\mathcal{I}_{l}}$ and $\mathcal{F}_{\mathcal{I}_{h}}$ are the visual encoders for high-resolution and low-resolution images, respectively. $\mathcal{F_{A}}$ denotes the mixture-of-resolution adapter (MR-Adapter). In Eq. 2, MRA adopts dual visual pathways to process high- and low- resolution images simultaneously. Then, a novel MR-Adapter is used to fuse the high-resolution information from the slow pathway to the fast one. Finally, the visual features of two resolutions are combined and processed by the LLM based on Eq. 1.

Figure: Figure 4: Illustration of the mixture-of-resolution adapter (MR-Adapter). MR-Adapter can dynamically embed the high-resolution features into the low-resolution pathway.
Refer to caption: x4.png

### 4.2 Dual Visual Pathways

As shown in Fig. 3, dual visual pathways are the key design of MRA, and their benefits are maximized from two aspects.

Visual functionality. Firstly, the dual visual pathways process images from macro- and micro-views, which is inspired by the visual system of human being (Merigan & Maunsell, ; Robertson & Lamb, ).
Particularly, Robertson & Lamb () find that the visual system processes local and global semantics via different pathways. Based on this finding, we adopt a similar mechanism to our MRA. Specifically, one visual pathway aims to capture fine-grained semantics from high-resolution images *i.e.*, processing images from local view. In contrast, the other pathway is designed to encode global information from low-resolution images, achieving a larger receptive field.

Visual alignment. Due to different resolutions, these two pathways often produce visual features of different shapes, impeding their quick alignments (Yu et al., ). To overcome this limitation, we adopt different downsampling rates for the low- and high-resolution pathways, respectively. Thus, their output features can keep the same spatial shape.

Based on the above observations, we design the dual visual pathways with a convolutional network (CNN) (Liu et al., ) and a vision transformer (ViT) (Dosovitskiy et al., ). Specifically, CNN is equipped with a downsampling stride of 32 to process high-resolution images. ViT encodes low-resolution images with a downsampling stride of 14. Notably, such designs also ensure the efficiency of MLLMs, where the high-resolution images are processed by the efficient CNN, and the number of visual tokens is also kept small via the large downsampling stride.

**Table 1: Performance and efficiency comparisons of LLaVA-HR and LLaVA-1.5 (Liu et al., ) at different resolutions. Except resolution, the other configurations of LLaVA-HR and LLaVA-1.5 remain the same. The training and inference costs are measured on NVIDIA A800s. “N/A” denotes that GPU memory overflows(^1^11 When memory overflows, we reduce the batch size and increase the gradient accumulation steps to train LLaVA-1.5.). “tokens/s” denotes the number of generated tokens per second.**
| Models | Resolution | Vision-Language Tasks | Training<br>Time $\downarrow$ | GPU<br>Memory $\downarrow$ | Inference<br>Speed $\uparrow$ |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| VQAv2 $\uparrow$ | TextVQA $\uparrow$ | MME $\uparrow$ | POPE $\uparrow$ |  |  |  |  |  |
| LLaVA-1.5 | 336 pix | 80.44 | 59.41 | 1461.17 | 86.2 | 15.6h | 28G | 23.8 tokens/s |
| LLaVA-HR (ours) | 384 pix | 80.47 | 59.63 | 1522.28 | 86.3 | 17.6h | 34G | 23.8 tokens/s |
| LLaVA-1.5 | 448 pix | 81.17 | 62.17 | 1493.12 | 87.2 | 19.4h | 49G | 19.9 tokens/s |
| LLaVA-HR (ours) | 768 pix | 81.80 | 64.36 | 1524.75 | 88.0 | 18.2h | 38G | 23.5 tokens/s |
| LLaVA-1.5 | 672 pix | 81.54 | 64.23 | 1498.71 | 87.9 | 31.8h | 79G | 12.7 tokens/s |
| LLaVA-HR (ours) | 1024 pix | 81.90 | 67.11 | 1554.90 | 87.6 | 20.7h | 40G | 19.7 tokens/s |
| LLaVA-1.5 | 1022 pix | 74.20 | 37.80 | 1266.90 | 84.4 | 69.4h | N/A(^1^11 When memory overflows, we reduce the batch size and increase the gradient accumulation steps to train LLaVA-1.5.) | 5.6 tokens/s |
| LLaVA-HR (ours) | 1536 pix | 81.82 | 67.96 | 1480.62 | 87.7 | 29.8h | 52G | 12.6 tokens/s |

### 4.3 Mixture-of-Resolution Adapter

To better collaborate the feature learning of two pathways, we propose a mixture-of-resolution adapter (MR-Adapter) for the fusion of visual information from different resolution images. In particular, given the visual features $\textbf{F}_{vh}\in\mathbb{R}^{h\times w\times d_{h}}$ extracted from a high-resolution image, we embed them into the low-resolution visual pathway by

$$ $\displaystyle\textbf{F}_{vl}^{\prime}=\textbf{F}_{vl}+f_{l}(\textbf{F}_{vl})+g \cdot f_{h}(\textbf{F}_{vh}).$ (3) $$

Here, $\textbf{F}_{vl}\in\mathbb{R}^{h\times w\times d_{l}}$ are the features from the low-resolution pathway. $f_{l}(\cdot)$ and $f_{h}(\cdot)$ denote two mapping modules, which are designed as a convolutional block and an MLP layer, respectively. $g$ is a dynamic score to control the weights of high-resolution information, defined by

$$ $\displaystyle g$ $\displaystyle=\delta(W_{2}\sigma(W_{1}f_{v})),$ (4) $\displaystyle f_{v}$ $\displaystyle=\frac{1}{h\times w}\sum_{i}^{h}\sum_{j}^{w}[f_{l}(\textbf{F}_{vl })^{i,j},f_{h}(\textbf{F}_{vh})^{i,j}].$ $$

Here, $[\cdot]$ denotes the concatenation operation, and $W_{1}\in\mathbb{R}^{2d\times\frac{d}{2}}$ and $W_{2}\in\mathbb{R}^{\frac{d}{2}\times d}$ are two projection matrices. $f_{v}\in\mathbb{R}^{d}$ is the pooled visual features. $\sigma$ and $\delta$ denote the activation function of GELU and Tanh, respectively.

As shown in Fig. 3, high-resolution information can be fused with the features in each block of ViT. In this case, the low-resolution features of ViT also contain rich semantics, improving the visual descriptive power of MLLMs.

### 4.4 The Deployment on MLLM

We apply MRA to a popular MLLM called LLaVA-1.5 (Liu et al., ), and construct a new model, namely LLaVA-HR. Its training consists of two stages, *i.e.*, low-resolution pre-training and high-resolution instruction tuning.

Stage 1: Low-Resolution Pre-training. Similar to LLaVA (Liu et al., ) and LLaVA-1.5 (Liu et al., ), this stage aims to optimize the projector to align the visual features with the word embeddings of LLM. Therefore, the image encoder and the LLM are frozen during pre-training. Besides, we adopt low resolutions for two pathways. In this stage, the MR-Adapter is not inserted, and output features of dual pathways are directly combined.

Stage 2: High-Resolution Instruction Tuning.
During instruction tuning, we greatly increase the resolution of the high-resolution pathway, *e.g.,* from 384$\times$ 384 to 1,024$\times$ 1,024. And the low-resolution one is also accordingly adjusted to ensure the visual alignment of two pathways, *e.g.,* from 336$\times$ 336 to 448$\times$ 448. Meanwhile, the MR-Adapter is then applied to connect two visual pathways. Different from the first training stage, the entire MLLM will be fully optimized to better accommodate high-resolution images.

## 5 Experiments

### 5.1 Evaluations and Metrics

Multimodal benchmarks for MLLM. We evaluate LLaVA-HR on four emerging multimodal benchmarks for MLLMs, including MME (Fu et al., ), POPE (Li et al., ), SEED (Li et al., ) and MM-VET (Yu et al., ). In particular, MME and MM-VET evaluate the multimodal perception and cognition abilities of MLLMs. SEED extends the modalities of evaluation to images and videos. POPE aims to evaluate the visual hallucinations of MLLMs. The metrics used in our paper follow their default settings. For MME, we follow LLaVA-1.5 to report the perception score.

Common vision-language benchmarks. We also evaluate LLaVA-HR on seven VL datasets, including VQAv2 (Goyal et al., ), GQA (Hudson & Manning, ), OKVQA (Marino et al., ), OCRVQA (Mishra et al., ), ScienceQA (Lu et al., ), VizWiz (Gurari et al., ) and TextVQA. In particular, ScienceQA (Lu et al., ), VizWiz (Gurari et al., ) and TextVQA are three zero-shot tasks, and their samples are not appeared in our training data. We report the accuracy on the test set of OCRVQA, the test set of VizWiz, and the val set of OKVQA. We organize samples of these tasks in instruction formats of LLaVA-1.5 (Liu et al., ).

### 5.2 Implementation Details

In LLaVA-HR, we use CLIP-ViT-L (Radford et al., ; Ilharco et al., ) and CLIP-ConvNeXt-L (Liu et al., ) as the dual visual paths to encode low- and high-resolution images, respectively. In LLaVA-HR-X, the CLIP-ConvNeXt-L is replaced with the stronger CLIP-ConvNeXt-XXL. The MR-Adapter is applied into the last three stages of ViT.
Following LLaVA-1.5, we first pre-train LLaVA-HR on LCS-558K (Liu et al., ), which contains 558k image-text pairs. During the pre-training stage, both the visual encoder and the LLM are frozen, and only the MLP projector is fine-tuned. AdamW (Kingma & Ba, ) is used as the optimizer, and the learning rate and batch size are set to 1e-3 and 256, respectively. Visual resolutions are set to 336$\times$336 and 384$\times$384 for the ViT and the CNN, respectively. During instruction tuning, we follow LLaVA-1.5 to use 665k VL instruction data. At this stage, the entire model is updated with a learning rate of 2e-5. Besides, we increase the resolution of ViT and CNN to 448$\times$448 and 1,024$\times$1,024, respectively. The training epoch is set to 1 for pre-training and instruction tuning.

**Table 2: Comparison of MRA and four baselines on LLaVA-HR. The visual resolution is set to about $\sim$760$\times$ 760.**
| Settings | VQAv2 | TextVQA | MME | POPE | Speed |
| --- | --- | --- | --- | --- | --- |
| ViT+ MLP | 81.0 | 63.2 | 1436.1 | 87.6 | 10.7 t/s |
| Conv+MLP | 80.3 | 64.6 | 1415.9 | 86.6 | 23.7 t/s |
| ViT+Resampler | 79.8 | 58.9 | 1403.8 | 85.8 | 27.6 t/s |
| ViT+Pooling+MLP | 80.6 | 59.6 | 1480.6 | 86.5 | 23.9 t/s |
| MRA (ours) | 81.8 | 64.4 | 1524.8 | 88.0 | 23.5 t/s |

**Table 3: Ablation study of mixture-of-resolution adaptation on LLaVA-HR. The resolution is 768 $\times$ 768. Our final setting is colored in gray. “L-Res Path.”, “H-Res Path.”, “Fusion Direct.”, “Struct.” and “Gate Fuct.” denote the low-resolution pathway, the high-resolution pathway, the fusion direction, the structure and the gate function, respectively.**
| Settings | Choices | VQAv2 | TextVQA | MME | POPE |
| --- | --- | --- | --- | --- | --- |
| L-Res<br>Path. | ViT-L | 81.8 | 64.4 | 1524.8 | 88.0 |
| None | 80.3 | 64.6 | 1415.9 | 86.6 |  |
| ViT-G | 81.7 | 65.3 | 1469.7 | 87.9 |  |
| H-Res<br>Path. | ConvNeXt-L | 81.8 | 64.4 | 1524.8 | 88.0 |
| None | 80.4 | 59.4 | 1461.2 | 86.2 |  |
| ConvNeXt-XXL | 82.3 | 66.5 | 1479.2 | 87.9 |  |
| Fusion<br>Direct. | High to Low | 81.8 | 64.4 | 1524.8 | 88.0 |
| Low to High | 81.0 | 62.8 | 1463.5 | 87.3 |  |
| Fusion<br>Type | Sum | 81.8 | 64.4 | 1524.8 | 88.0 |
| Concat | 81.7 | 64.7 | 1508.8 | 87.3 |  |
| Struct. | mlp-conv | 81.8 | 64.4 | 1524.8 | 88.0 |
| conv-conv | 81.6 | 64.6 | 1499.0 | 87.7 |  |
| conv-mlp | 81.5 | 64.2 | 1517.9 | 87.6 |  |
| Gate<br>Funct. | Tanh | 81.8 | 64.4 | 1524.8 | 88.0 |
| Sigmoid | 81.7 | 64.3 | 1567.9 | 86.9 |  |
| H-sigmoid | 81.6 | 64.4 | 1525.9 | 87.8 |  |

**Table 4: Comparison with existing methods on four MLLM benchmarks. “Param.”, “Res.” and “Data” refer to the total parameters, the visual resolution and the number of training data, respectively. “t/s” refers to tokens per second.**
| Method | Settings | Multimodal Benchmarks | Inference |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Param. | Res. | Data | MME | POPE | SEED | MM-Vet | Speed |  |
| BLIP-2 | 14.2B | 224 | 129M | 1293.8 | 85.3 | 46.4 | 22.4 | - |
| InstructBLIP | 8.2B | 224 | 130M | - | - | 53.4 | 26.2 | 22.6 t/s |
| InstructBLIP | 14.2B | 224 | 130M | 1212.8 | 78.9 | - | 25.6 | - |
| QWen-VL-Chat | 9.6B | 448 | 1.4B | 1487.5 | - | 58.2 | - | 17.0 t/s |
| Fuyu-8B | 8B | $\sim$600 | - | 728.6 | 74.1 | - | 21.4 | 15.6 t/s |
| mPLUG-Owl2 | 8.2B | 448 | 400M | 1450.2 | - | 57.8 | 36.2 | 19.6 t/s |
| LLaVA-1.5 | 7.2B | 336 | 1.2M | 1510.7 | 85.9 | 58.6 | 30.5 | 23.8 t/s |
| LLaVA-1.5 | 13.2B | 336 | 1.2M | 1531.3 | 85.9 | 61.6 | 35.4 | - |
| LLaVA-HR | 7.4B | 1024 | 1.2M | 1554.9 | 87.6 | 64.2 | 31.2 | 19.7 t/s |
| LLaVA-HR | 13.4B | 1024 | 1.2M | 1540.9 | 87.8 | 64.5 | 34.8 | 15.0 t/s |
| LLaVA-HR-X | 14B | 1024 | 1.2M | 1487.3 | 88.0 | 65.3 | 35.5 | 12.9 t/s |

**Table 5: Comparison with existing methods on seven vision-language tasks. SQA${}^{I}$ refers to the IMG subset of ScienceQA.**
| Method | Settings | In-domain Tasks | Zero-shot Tasks | Infer. |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Param. | Res. | Data | VQAv2 | GQA | OKVQA | OCRVQA | SQA${}^{I}$ | VizWiz | TextVQA | Speed |  |
| BLIP-2 | 14.2B | 224 | 129M | 41.0 | 41.0 | 45.9 | 40.6 | 61.0 | 19.6 | 42.5 | - |
| InstructBLIP | 8.2B | 224 | 130M | - | 49.2 | - | - | 60.5 | 34.5 | 50.1 | 22.6 t/s |
| InstructBLIP | 14.2B | 224 | 130M | - | 49.5 | - | 44.8 | 63.1 | 33.4 | 50.7 | - |
| Shikra | 13.2B | 224 | 6.1M | 77.4 | - | - | - | - | - | - | - |
| IDEFICS-9B | 9B | 224 | 354M | 50.9 | - | 38.4 | - | - | 35.5 | 25.9 | 30.5 t/s |
| IDEFICS-80B | 80B | 224 | 354M | 60.0 | - | 45.2 | - | - | 36.0 | 30.9 | - |
| Qwen-VL-Chat | 9.6B | 448 | 1.4B | 78.2 | 57.5 | 56.6 | 70.5 | 68.2 | 38.9 | 61.5 | 17.0 t/s |
| Fuyu-8B | 8B | $\sim$600 | - | 74.2 | - | 60.6 | - | - | - | - | 15.6 t/s |
| mPLUG-Owl2 | 8.2B | 448 | 400M | 79.4 | 56.1 | 57.7 | - | 68.7 | 54.5 | 58.2 | 19.6 t/s |
| LLaVA-1.5 | 7.2B | 336 | 1.2M | 78.5 | 62.0 | - | - | 66.8 | 50.0 | 58.2 | 23.8 t/s |
| LLaVA-1.5 | 13.2B | 336 | 1.2M | 80.0 | 63.3 | - | - | 71.6 | 53.6 | 61.3 | - |
| LLaVA-HR | 7.4B | 1024 | 1.2M | 81.9 | 64.2 | 58.9 | 68.4 | 65.1 | 48.7 | 67.1 | 19.7 t/s |
| LLaVA-HR | 13.4B | 1024 | 1.2M | 82.3 | 64.8 | 60.7 | 67.7 | 68.1 | 57.9 | 68.1 | 15.0 t/s |
| LLaVA-HR-X | 14B | 1024 | 1.2M | 82.6 | 65.2 | 61.5 | 69.0 | 68.0 | 56.6 | 70.9 | 12.9 t/s |

### 5.3 Experimental Results

#### 5.3.1 Quantitative Analysis

Comparison with baselines.
In Tab. 1, we compare the performance and efficiency of LLaVA-HR with LLaVA-1.5 (Liu et al., ) with different image resolutions. From this table, we observe that increasing image resolution obviously improves the performance of two models on four tasks, *e.g.,* +4.8% of LLaVA-1.5 on TextVQA. However, the performance of LLaVA-1.5 drops significantly at the resolution of 1,024$\times$1,024. To explain, the number of visual tokens greatly exceeds the pre-trained context length of the LLM, which easily causes the instability during training. In contrast, the performance of LLaVA-HR is consistently improved from 384 $\times$ 384 resolution to 1,024 $\times$ 1,024 resolution. Besides, the total gain of LLaVA-HR is more obvious than that of LLaVA-1.5 (Liu et al., ), *e.g.,* +8.33% of LLaVA-HR vs. +4.82% of LLaVA-1.5, greatly confirming the effectiveness of MRA.

In Tab. 2, we further compare four common baselines with the similar resolution, *i.e.,* $\sim$760$\times$760. “ViT+MLP” is the default setting of LLaVA-1.5 as the reference. “Conv+MLP” replaces the visual backbone with ConvNeXt (Liu et al., ), which uses a larger downsampling rate to reduce the number of visual tokens. “ViT+Resampler” and “ViT+Pooling+MLP” refer to the two pooling strategies for reducing the number of visual tokens. As can be seen, all compared methods are inferior to LLaVA-HR. In particular, using a convolutional network as the visual backbone greatly improves efficiency, but its performance still lags behind LLaVA-HR by a large margin, *e.g.,* -108.9 on MME (Fu et al., ). Similarly, “ViT+Resampler” and “ViT+Pooling+MLP” also sacrifice performance for efficiency. Overall, these comparisons further confirm the designs of MRA.

Despite effectiveness, the expenditure of LLaVA-HR is also cost-effective. In particular, increasing resolution from 384 $\times$ 384 to 1,024 $\times$ 1,024 slows down the training and inference of LLaVA-1.5 by 344.8% and 325%, respectively. However, these costs are reduced to only 17.6% and 20.8% in LLaVA-HR. Despite better performance, the training and inference speeds of LLaVA-HR are three times faster than LLaVA-1.5. Besides, the costs of GPU memory also remain cheap for LLaVA-HR. For example, adapting the resolution of 1,536 $\times$ 1,536 for LLaVA-HR only consumes 52G GPU memory, but the same settings for LLaVA-1.5 will cause GPU memory overflow. These results greatly confirm the efficiency of our MRA and LLaVA-HR.

Figure: Figure 5: Visualizations of LLaVA-HR and existing MLLMs. Subfig-(a) shows that high image resolution greatly improves the capability of MLLMs on fine-grained VL tasks. In Subfig-(b), LLaVA-HR-X demonstrates the comparable ability with GPT4-V in visual information extraction(^2^22For privacy reasons, we blur some key personal information.). Correct and incorrect answers are colored in green and red, respectively.
Refer to caption: x5.png

Ablation studies.
In Tab. 3, we conduct comprehensive ablation studies for MRA on four VL benchmarks. Firstly, we validate the different designs of the dual visual pathways. From these results, we find that removing one pathway will lead to significant performance drops, *e.g.,* -1.5% on VQAv2. Besides, scaling up the high-resolution encoder brings more gains than that of the low-resolution one, *e.g.,* +2.1% vs. +0.9% on TextVQA. We assume that the stronger high-resolution image encoder can better capture the fine-grained visual information. Then, we ablate different fusion directions and strategies in MRA. Specifically, changing the fusion direction obviously degenerates the performance, *e.g.,* -61.3 on MME. Finally, we ablate the designs of the mixture-of-resolution adapter. Specifically, the best choices of mapping modules for the low- and high-resolution pathways are convolution blocks and MLP blocks, respectively. Besides, the choices of gating function also affect performance and the tanh function perform the best. These ablations further confirm the designs of MR-Adapter.

Comparison with existing MLLMs.
In Tab. 4 - 5, we compare LLaVA-HR with existing MLLMs on 11 VL tasks. On the four MLLM benchmarks, we observe comprehensive advantages of LLaVA-HR against existing MLLMs. In particular, LLaVA-HR achieves 1554.9 scores in MME benchmark, outperforming LLaVA-1.5 by +23.6. On POPE, a benchmark including video evaluations, LLaVA-HR-X still outperforms existing MLLMs by a large margin, *i.e.,* +3.7% gains. Besides, LLaVA-HR achieves the best performance on the benchmark for visual hallucinations, *i.e.,* POPE, suggesting that its visual hallucinations are greatly alleviated. Notably, Fuyu-8b (Fuyu-8B, ) is capable of high-resolution images, but its performance is much inferior to LLaVA-HR, *e.g.,* 728.6 vs. 1554.9 on MME.

Tab. 5 gives the performance comparison on common VL tasks. On in-domain tasks, LLaVA-HR achieves the best results on three tasks, *e.g.,* 82.6 on VQAv2 and 61.5 on OKVQA. On OCRVQA, Qwen-VL-Chat collects more in-domain data for training, so it performs better than LLaVA-HR. Under the zero-shot setting, we can observe more significant advantages of LLaVA-HR on the fine-grained tasks, *e.g.,* VizWiz and TextVQA. Most notably, even Qwen-VL-Chat is pre-trained with 24.8M OCR samples, it still performs worse than LLaVA-HR-X on TextVQA. These results suggest the significance of high resolution for these tasks. In contrast, most images of ScienceQA are synthetic and of low resolution, so the advantages of LLaVA-HR are not obvious. Overall, these results greatly confirm the effectiveness and generalization of LLaVA-HR and our MRA.

#### 5.3.2 Qualitative Experiments

In Fig 2 (a), we compare the predictions of LLaVA-HR with different resolutions. The visualizations show that higher image resolution obviously improves the capability of MLLMs on fine-grained tasks. For example, LLaVA-HR with a resolution of 1,024 $\times$ 1,024 can well capture granular visual content, *e.g.,* the tiny boat in the first example. Besides, high image resolution also enables LLaVA-HR a
stronger ability of text recognition. For instance, the small and blurred phrase of “wo ich wohne” in the second example are correctly identified by the high-resolution LLaVA-HR. These results greatly confirm the significance of high image resolution in addressing visual shortcoming.
In Fig 2 (b), we further compare the predictions of LLaVA-HR-X, LLaVA-1.5 (Liu et al., ) and GPT4-V (OpenAI, ) in visual information extraction. Notably, LLaVA-HR-X shows a comparable ability with GPT4-V on this challenging task. As shown in Fig 2 (b), LLaVA-HR-X and GPT4-V can correctly extract almost all visual content of the driver license and organize it in JSON format. Compared to GPT4-V, LLaVA-HR-X also correctly identifies the hair color of the person, which requires fine-grained visual reasoning. In contrast, LLaVA-1.5 can only recognize simple visual content like “class” and “SEX”, and fail to extract most visual information. These results further validate the effectiveness of MRA in addressing visual shortcoming of MLLMs.

## 6 Conclusion

In this paper, we study the visual shortcoming of MLLMs from the perspective of image resolution, and propose a novel and efficient method for high-resolution adaptations of MLLMs, namely mixture-of-resolution adaptation (MRA). MRA adopts dual visual pathways to process images of both high and low resolutions, where high-resolution information is embeded into the low-resolution modeling via the novel mixture-of-resolution adapters (MR-Adapters). We apply MRA to a popular MLLM called LLaVA-1.5, and construct a new high-resolution MLLM, termed LLaVA-HR. Experimental results not only validate the effectiveness of LLaVA-HR in addressing visual shortcoming, but also confirm its remarkable efficiency against existing MLLMs.

##### Acknowledgements.

This work was supported by National Key R&D Program of China (No.2022ZD0118201) , the National Science Fund for Distinguished Young Scholars (No.62025603), the National Natural Science Foundation of China (No. U21B2037, No. U22B2051, No. 62176222, No. 62176223, No. 62176226, No. 62072386, No. 62072387, No. 62072389, No. 62002305 and No. 62272401), the Natural Science Foundation of Fujian Province of China (No.2021J01002, No.2022J06001), and the China Fundamental Research Funds for the Central Universities (Grant No. 20720220068).

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: 1. In section4.3(line 258), the statement, global average pooling is confusion, is the features are pooled into 1 global token? If so, it seems to be not consistent with figures. Please clarify the exact dimensions of fv after global average pooling.
2. In Table 1, resizing LLaVA-1.5 to 672 pix achieves close performance with 768pix version of LLaVA-HR, is there a direct comparision between 768-pix version of them?
3. In table 2, there is an ablation of "tune vision" referring to finetune vision encoder. However, I think the vision encoder in LLaVA-1.5 is fixed, can you provide a detailed description about this. For example, implementation and aim of tuning vision encoder.
4. LLaVA-HR is proposed to process input resolution of 1024, what if input images larger than 1024. Is there any extended experiments for even larger images such as 4K ones.
5. What do you mean by "stages" in vision transformers? And, currently only final features from ConvNext is utilized, is there any experiments of multi-stage feature integration for that of CNN encoder?
soundness: 3
strengths: This work proposed a novel method to increase resolutions of MLLMs, which is an important problem in the field and critical in fine-grained  vision tasks. Without large modification of training recipe and computational cost of its baseline, LLaVA-1.5. 
Evalutions are conducted on many existing benchmarks and performance of LLaVA-HR is quite impressive. Besides, the computational cost involved is quite small compared with related works.
summary: This paper aims to enhance MLLM by enlarging resolution of input images. By combining features from ViT and a CNN encoder through an adapter, performances of MLLM are improved a lot. Meanwhile, fusing high-resolution features from convolution-based encoder into low-resolution features from transformer-based encoder does not increase vision tokens to LLM decoder, so that additional computational cost is low. Proposed LLaVA-HR increases effective resolution for MLLM to 1024 and outperforms concurrent MLLMs.
weaknesses: Please see as in questions.

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: 1. There are several micro-designs in the Mixture-of-Resolution Adapter, including $\mathcal{F}_l$, $\mathcal{F}_h$, and the gate function. Why do we choose a conv layer for $\mathcal{F}_l$, an MLP layer for $\mathcal{F}_h$? Are these layers and functions necessary? Please provide some analyses.

2. In the Mixture-of-Resolution Adapter, the authors choose the addition operation to fuse features of different resolutions. (Deformable) Cross Attention is also an option. I wonder which method is better?
soundness: 3
strengths: 1. The paper is well-written and easy to follow.
2. The comparison of MRA and other high-resolution adaptation solutions is clear, highlighting the effectiveness of the dual visual pathways.
3. The experiments are well-conducted and quite comprehensive.
4. The study demonstrates strong performance on most datasets compared with other MLLMs.
summary: In this paper, the authors propose the Mixture-of-Resolution Adaptation method to embed the high-resolution features into the low-resolution pathway. The MRA enhances the visual perception ability in MLLMs, and allow them to benefit from high-resolution visual inputs with reduced computational cost. Extensive experiments demonstrate the effectiveness of the MRA.
weaknesses: 1. In Table 1, the MRA is compared to other high-resolution adaptation methods that use a single visual pathway. However, the introduction of a new visual encoder in the MRA raises concerns about the fairness of this comparison. Could the authors provide a baseline that uses dual visual pathways without the MR-Adapter?
2. The analyses of the MRA’s architecture and design details are insufficient, particularly regarding $\mathcal{F}_l$, $\mathcal{F}_h$, and the gate function. Could the authors provide ablation studies on these components?
3. The main novelty of the paper appears to be the Mixture-of-Resolution Adapter. While the application of dual visual pathways for high-resolution adaptation in MLLMs is innovative, the overall contribution of the paper seems somewhat insufficient. If MR-Adapter could integrate a wider variety of low- and high- resolution visual encoders, its contribution would be significantly enhanced.

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: > ### 1. Clarification on Visual Encoder Notation

In line 206, it states that $F_{I_l}$ and $F_{I_h}$ are visual encoders for high- and low-resolution images, which seems to be a typo. The correct notation should reflect that $F_{I_l}$ and $F_{I_h}$ correspond specifically to low- and high-resolution encoders, respectively.

> ### 2. MR-Adapter Placement in ViT Architecture

Figure 2 shows the MR-Adapter is applied starting from the second stage of the ViT architecture. Does this mean the initial stage of the ViT does not utilize high-resolution features? Clarifying this could help illustrate the feature extraction flow more clearly.

> ### 3. Implementation of LLaVA-1.5-448

For LLaVA-1.5-448, only the image resolution is modified at the fine-tuning stage. Have you considered modifying the visual backbone from ViT-336 to ViT-448 and retraining it for both pre-training and fine-tuning? This comparison could provide insight into performance differences when using higher resolution throughout the model’s entire training process.

> ### 4. $SEED^{img}$ Performance Comparison

Could you provide the $SEED^{img}$ performance for LLaVA-1.5, LLaVA-1.5-448, and LLaVA-NeXT? This metric would help evaluate relative image-processing capabilities across these models.
soundness: 3
strengths: 1. The paper is well-written and easy to follow.

2. Figures 2 and 3 are effectively designed and enhance understanding of the framework.

3. The ablation study is solid to reveal the contribution of component.
summary: This paper presents a new approach for efficient multimodal large language models (MLLMs) by addressing the high computational cost of processing high-resolution images. The authors introduce Mixture-of-Resolution Adaptation (MRA), a method that combines both low- and high-resolution visual features to enhance model efficiency without compromising visual recognition quality. MRA uses two visual pathways: one for low-resolution and one for high-resolution images, with novel mixture-of-resolution adapters (MR-Adapters) that embed high-resolution information into the low-resolution pathway. This design significantly reduces input sequence length and computational load.

The authors apply MRA to the LLaVA model, resulting in an improved version called LLaVA-HR, which demonstrates superior performance across 15 out of 17 vision-language (VL) tasks, including a 5.2% increase in accuracy on TextVQA. Furthermore, LLaVA-HR maintains efficient training and inference times, showing improvements over LLaVA-NeXT.
weaknesses: > ### 1. LImited performance imprvement.

The performance gains with MRA are modest. The low-resolution branch operates at 448×448, so the appropriate baseline is LLaVA-1.5 with 448-pixel resizing. Compared to this baseline, the improvements MRA achieves are minimal (e.g., +0.7 on VQA v2, +31 on MME, and +0.8 on POPE). Training cost and inference speed are also similar between MRA and LLaVA-1.5-448, reducing the practical benefit.

> ### 2. Limited novelty

The dual-pathway, high-and-low-resolution approach isn’t particularly new. Similar strategies have been explored in other works, such as Mini-Gemini and CogAgent, yet the authors do not compare their method with these models. Explicitly differentiating MRA from these approaches would help clarify its unique contributions.

> ### 3. Limited generalizability

The authors apply MRA solely to LLaVA-1.5. Expanding the evaluation to other MLLMs, like Qwen-VL, would strengthen claims of the method’s generalizability across architectures.


[1] CogAgent: A Visual Language Model for GUI Agents
[2] Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: As mentioned, in Table 1, it seems that there is no significant gap between ‘Avg. Pooling’ and the proposed MRA for the VQAv2 task, which is perplexing. The paper should explain the experimental phenomenon.
2. The paper should carry out a qualitative experiment between the proposed MRA and the model variant in Table 2.
soundness: 3
strengths: 1. The paper aims to explore the high-resolution adaptation for MLLMs, which is crucial and engaging.
2. The paper is well written and easy to follow.
3. The paper is well motivated and the proposed MRA appears reasonable.
summary: This paper focuses on the efficient high-resolution adaptation for multimodal large language models (MLLMs) and proposes a mixture-of-resolution adaptation (MRA) method for MLLMs. To be specific, the proposed MRA employs two visual pathways for images of different resolutions, where high-resolution visual information is embedded into the low-resolution pathway via the mixture-of-resolution adapters. Besides, the paper conducts extensive experiments to verify the effectiveness of the proposed model.
weaknesses: 1. As demonstrated in Table 1, it seems that there is no significant gap between ‘Avg. Pooling’ and the proposed MRA for the VQAv2 task, which is perplexing. The paper should explain the experimental phenomenon.
2. The paper should carry out a qualitative experiment between the proposed MRA and the model variant in Table 2.
3. The paper fails to clarify the version of LLaVA-1.5 used in Figure 4.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: See Weakness.
soundness: 3
strengths: - Unlike previous strategies that divide high-resolution images into sub-images, this paper introduces an innovative dual visual pathway structure, offering a fresh perspective for high-resolution adaptation. The MR-Adapter effectively embeds high-resolution information into the low-resolution pathway, introducing a new adaptation mechanism within the visual processing framework of MLLMs. This design overcomes the efficiency limitations of traditional high-resolution processing.

- The paper conducts extensive experiments across multiple vision-language tasks, providing a range of comparisons, with promising results.

- The writing is clear and easy to follow. It effectively highlights MRA's performance gains and efficiency advantages across different tasks, helping readers fully understand the model’s effectiveness and strengths.
summary: This paper introduces an novel high-resolution adaptation method for multimodal large language models (MLLMs), termed Mixture-of-Resolution Adaptation (MRA). MRA employs a dual visual pathway design to process high- and low-resolution images simultaneously from both macro and micro perspectives, while integrating high-resolution information into the low-resolution pathway through the Mixture-of-Resolution Adapter (MR-Adapter). This approach reduces the number of visual tokens while preserving rich visual semantics, significantly enhancing the model's visual descriptive power.
weaknesses: 1. The processing of both low-resolution and high-resolution images in the paper is mainly square-based, such as 448x448 and 1024x1024. Is there any adaptation mechanism for handling images with different aspect ratios? Would processing high-resolution images in a way that matches the input image's aspect ratio lead to better performance?

2. For high-resolution image inputs, we are more focused on improvements in OCR-related tasks. The results for OCRVQA in Table 5 don’t seem to be the best. Additionally, Table 6 only presents results for LLaVA-HR+, but it lacks results for LLaVA-HR-7B, LLaVA-HR-13B, and LLaVA-HR-X with less training data. It would be helpful to include these results to better illustrate the impact of MRA on OCR-related tasks.

3. Could the authors further explain why the MR-Adapter is inserted in the last 3 stages? What is the design principle behind this decision? Could it be inserted in the earlier stages instead?

Meta-review: This paper proposes a mixture-of-resolution adaption method for multimodal large language model (MLLM). It consists of two visual pathways for images of different resolutions, ViT/ConveNext for low/high-resolution. The information from high-res input is adapted to low-res features by using a design of mixture-of-resolution adapter, which won't increase the token length when feeding to the LLMs, thus marginal computational overhead when using high-res image input. The experiments have shown the effectiveness and efficiency of the proposed method, and better performance than other MLLMs. 

The contributions include: 1) the method is somehow novel; 2) the investigated problem is important; 3) the paper is well written and easy to understand; 4) the experimental results look good. The main concerns are 1) limited novelty (Mfi4); 2) incremental improvement on some datasets; 3) limited generation on model architecture; 4) missing additional ablations/clarification. Although most of them are well resolved by the rebuttal, some are left, including the novelty given existing work, and generalization to other MLLM architecture (only on LLaVA series). The AC also has the same concerns on these. In addition, the AC is quite concerned on why using heterogeneous vision encoders instead of homogeneous ones, if the main motivation is on mixture of resolution. Resolution is one of fundamental problems in computer vision, but this paper doesn't seem to dive deep enough. For example, ViT is not scale invariant, and simply increasing the resolution to too large one will definitely decreases the performance. Those naive resizing experiments without looking into ViT pretraining don't make much sense to me. In addition, the AC found many of the experimental details are missing. For example, what's "Resamper" in Table 1; why Resamper produces 64 tokens but compared with MRA with 576 tokens; how do you "+ConvNeXT" in the response to b6Tu's comment #1? Without the details, it is difficult to understand the values of those numbers. 

However, most of the reviewers are happy with this submission and rebuttal. The AC is OK to accept it. But the authors are strongly recommended to fix those issues and make the paper more clear.

Decision: Accept (Poster)