Title: MoVA: Adapting Mixture of Vision Experts to Multimodal Context
Venue: NeurIPS 2024
Accepted: yes
Keywords: Multimodal large language model

Vision encoder

Mixture-of-expert
Abstract: As the key component in multimodal large language models (MLLMs), the ability of the visual encoder greatly affects MLLM's understanding on diverse image content. Although some large-scale pretrained vision encoders such as vision encoders in CLIP and DINOv2 have brought promising performance, we found that there is still no single vision encoder that can dominate various image content understanding, e.g., the CLIP vision encoder leads to outstanding results on general image understanding but poor performance on document or chart content. To alleviate the bias of CLIP vision encoder, we first delve into the inherent behavior of different pre-trained vision encoders and then propose the MoVA, a powerful and novel MLLM, adaptively routing and fusing task-specific vision experts with a coarse-to-fine mechanism. In the coarse-grained stage, we design a context-aware expert routing strategy to dynamically select the most suitable vision experts according to the user instruction, input image, and expertise of vision experts. This benefits from the powerful model function understanding ability of the large language model (LLM). In the fine-grained stage, we elaborately conduct the mixture-of-vision-expert adapter (MoV-Adapter) to extract and fuse task-specific knowledge from various experts. This coarse-to-fine paradigm effectively leverages representations from experts based on multimodal context and model expertise, further enhancing the generalization ability. We conduct extensive experiments to evaluate the effectiveness of the proposed approach. Without any bells and whistles, MoVA can achieve significant performance gains over current state-of-the-art methods in a wide range of challenging multimodal benchmarks.

MoVA: Adapting Mixture of Vision Experts to Multimodal Context

Abstract

As the key component in multimodal large language models (MLLMs), the ability of the visual encoder greatly affects MLLM's understanding on diverse image content. Although some large-scale pretrained vision encoders such as vision encoders in CLIP and DINOv2 have brought promising performance, we found that there is still no single vision encoder that can dominate various image content understanding, e.g., the CLIP vision encoder leads to outstanding results on general image understanding but poor performance on document or chart content. To alleviate the bias of CLIP vision encoder, we first delve into the inherent behavior of different pre-trained vision encoders and then propose the MoVA, a powerful and novel MLLM, adaptively routing and fusing task-specific vision experts with a coarse-to-fine mechanism. In the coarse-grained stage, we design a contextaware expert routing strategy to dynamically select the most suitable vision experts according to the user instruction, input image, and expertise of vision experts. This benefits from the powerful model function understanding ability of the large language model (LLM). In the fine-grained stage, we elaborately conduct the mixture-of-vision-expert adapter (MoV-Adapter) to extract and fuse task-specific knowledge from various experts. This coarse-to-fine paradigm effectively leverages representations from experts based on multimodal context and model expertise, further enhancing the generalization ability. We conduct extensive experiments to evaluate the effectiveness of the proposed approach. Without any bells and whistles, MoVA can achieve significant performance gains over current state-ofthe-art methods in a wide range of challenging multimodal benchmarks. Codes and models are available at https://github.com/TempleX98/MoVA .

1 Introduction

Significant achievements in multimodal large language models (MLLMs) [1, 2, 3, 4, 5, 6, 7] have been witnessed due to their remarkable proficiency in solving open-world tasks. MLLMs acquire visual perception capacity while inheriting sophisticated reasoning abilities and knowledge from large language models (LLMs) [8, 9, 10]. The core idea behind MLLMs is projecting the vision representation into an LLM through a projector, facilitating a general-purpose multimodal understanding.

General multimodal understanding requires comprehending complex image contexts across various tasks and scenarios. The CLIP [11] vision encoder, pre-trained on large-scale image-text pairs with a contrastive loss, is widely considered as a flexible and popular choice among the latest leading MLLMs. However, training data and optimization target of the vision encoder determine its

inconsistent performance across tasks and scenarios, which will bias the generalization of multimodal large language models. For instance, MLLMs with a single CLIP vision encoder usually perform poorly on fine-grained tasks such as grounding and optical character recognition (OCR) [12]. Several works have attempted to incorporate extra state-of-the-art vision encoder experts to cope with the challenge. For example, both SPHINX [13] and MoF [14] integrate vision self-supervised learning features of DINOv2 [15] with MLLMs to enhance their visual grounding capabilities. Vary [12] introduces a new vision encoder expert for improved fine-grained document and chart parsing ability. Intuitively, it is necessary to explore the utilization of more task-specific vision encoder experts in MLLMs to promote model generalization across various domains.

We aim to start the exploration through empirical analysis of readily available vision experts. In particular, we focus on the multimodal capabilities of seven distinct state-of-the-art vision encoders based on LLaVA-1.5-7B [28]. The results in Table 1 reveal that MLLMs with these task-specific vision encoders achieve optimal performance in their respective area. Concurrently, we note that the plain fusion (concatenation) of vision encoder experts adopted in previous works [13] would not bring consistent improvement compared with the single task-specific vision expert in its proficient task. The inherent bias of each expert introduces biased information and leads to performance degradation in the plain fusion paradigm. For example, DINOv2 serves as an expert in visual grounding but performs poorly at text-oriented tasks. Representation of DINOv2 would be regarded as biased information in text-related scenarios so incorporating DINOv2 for these tasks would inevitably cause performance decrease. Consequently, a flexible method of vision encoder ensemble that dynamically activates and weights context-relevant task-specific vision experts can fully unleash the capacity of these models while avoiding model bias.

In this paper, we propose MoVA, a powerful MLLM, adaptively routing and fusing task-specific vision experts with a coarse-to-fine mechanism. Inspired by the powerful tool-use capabilities of LLM [29], the coarse-grained context-aware expert routing aims to employ LLM to select vision experts with strong relevance to the user's image and instruction from the expert model pool. Thanks to the strong generalization ability of LLM, we also can perform model routing for vision experts in open scenarios. The fine-grained expert fusion facilitates better extraction and integration of expert representations based on multimodal context. Specifically, the expert knowledge extractor in the mixture-of-vision-expert adapter (MoV-Adapter) will extract diverse task-specific knowledge from various vision experts through mixture-of-expert (MoE) cross-attention layers. The dynamic gating network can allocate precise expert-wise soft weights for the integration of extracted taskspecific knowledge. Under the coarse-to-fine paradigm, we provide a flexible and effective manner of leveraging representation from experts based on multimodal context and model expertise, further enhancing the model generalization ability. As presented in Table 1, MoV A can preserve the optimal performance of a single relevant vision encoder by ignoring non-relevant experts on the GQA, POPE, and REC task. Besides, MoVA can further boost performances via the fine-grained fusion of multiple relevant vision experts on other tasks.

We conduct comprehensive experiments on various benchmarks to evaluate the effectiveness of MoVA, including MLLM benchmarks, visual question answering (VQA), visual grounding, and biomedical understanding. Without any bells and whistles, MoVA can achieve significant performance gains over current state-of-the-art methods.

The contributions of this work are three-fold: (i) By analyzing the performance of individual vision encoders versus the plain fusion of multiple encoders across various tasks, we reveal that the inherent bias of each vision encoder can diminish its generalization ability across other irrelevant domains. (ii) We propose MoVA, a powerful MLLM composed of coarse-grained context-aware expert routing and fine-grained expert fusion with MoV-Adapter. Based on multimodal context and model expertise, MoVA fully leverages representation from multiple context-relevant vision experts flexibly while avoiding biased information of irrelevant experts. (iii) We demonstrate the effectiveness of each component in MoVA by elaborate ablation studies. MoVA can achieve significant performance gains over state-of-the-art methods in a wide range of challenging benchmarks.

2 Related Work

Multimodal architectures [1, 3, 6, 30, 31, 32, 33, 34], optimization paradigm [35, 36], applications [37, 38, 39, 40, 41], and benchmarks [42, 43, 44, 45, 46, 47, 48] have recently achieved remarkable progress and garnered unprecedented attention within the academic community. Multimodal large language models (MLLMs) usually leverage the alignment from visual features to the linguistic feature space to achieve superior vision-language understanding capabilities based on off-the-shelf LLMs and vision encoders. CLIP vision encoder [11], which is trained in contrastive learning from billions of diverse image-text pairs [49, 50], is widely used among these works. For example, LLaVA [3] adopts an MLP projector to align visual tokens from the frozen CLIP vision encoder to the embedding layer of LLM. However, The representation from CLIP exhibits strong discriminative abilities in classification and recognition but only has limited performance on downstream tasks like location and relation understanding [51]. To break through this bottleneck, some works [4, 52] turn to unlock the CLIP vision encoder and further fine-tune the parameter with training data for downstream tasks. For instance, Qwen-VL [6] collected massive training data for grounding and OCR to jointly optimize the CLIP vision encoder and LLM. Recent works propose to involve an extra frozen vision encoder to enhance the performance of MLLMs. SPHINX [13] is one of the pioneers, where grounding capabilities have been significantly improved with the assistance of the DINOv2 [15]. Vary [12] introduces an extra encoder training on large-scale charts and document data to improve the performance on related downstream tasks.

3 MoVA Methodology

3.1 Overview

MoVA comprises five key components: (i) a pre-trained large language model (LLM) that generates accurate responses given the image tokens and instructions; (ii) a base vision encoder; (iii) vision experts that generate task-specific vision latent features; (iv) mixture-of-vision-expert adapter (MoVAdapter) that performs fine-grained expert fusion based on the multimodal context.

As illustrated in Figure 1, MoV A consists of two stages: coarse-grained context-ware expert routing and fine-grained expert fusion with MoV-Adapter. First, our coarse-grained context-ware expert routing leverages the tool-use capabilities of LLM, routing the most appropriate experts from N expert candidates via LLM to help the model answer the user's question. In the second stage, we turn to enhance the visual representation with a novel MoV-Adapter module in a fine-grained manner. More specifically, we leverage the mixture-of-expert (MoE) cross-attention layers to extract the taskspecific knowledge of representations from chosen experts. Meanwhile, the dynamic gating network in MoV-Adapter can allocate soft weights to the extracted knowledge of each expert according to the input image and instruction. Then the extracted knowledge can be effectively integrated into the foundational representation of the base vision encoder. Finally, the enhanced visual representation with instruction tokens is fed to the LLM to generate an accurate response. In Section 3.2 and Section 3.3, we will focus on our core contributions, the context-aware expert routing strategy, and the expert fusion with MoV-Adapter. In Section 3.4, we will introduce the training process.

<system prompt>

<model descriptions>

Here is user question:

###

Where is the red sign

and what does it say?

###

Identify and select

models that will best…

Base Encoder

Downsample

Step 1:

Step 2:

Context-aware expert routing

Expert fusion with MoV-Adapter

Q: Where is the red

sign and what does it

say?

Q: Where is the red

sign and what does it

say?

……

Expert 1

visual grounding

Expert 2

text recognition

Expert N

chart processing

Vision Experts

Selection: Expert 1, Expert 2

Base Encoder

MoV-Adapter

1

STOP

STOP

Pretrained Vision Encoders and LLM. The vision encoders in MoVA consist of a base encoder and multiple task-specific vision encoder experts. We choose the pre-trained CLIP ViT-L-336px as the base encoder. Our vision experts include several state-of-the-art task-specific encoders: DINOv2, CoDETR, SAM, Pix2Struct, Deplot, Vary, and BiomedCLIP. The corresponding expertise is presented in Table 1. For example, both Pix2Struct and Vary will be used when the user asks the MLLM to scan the document image. MoVA is flexible and easy to generalize to all decoder-only LLMs. We mainly consider Vicuna-7B [8], Llama3-8B 3 , and Yi-34B [53] as our language models in this work.

3.2 Coarse-grained Context-aware Expert Routing

Pipeline of Context-aware Routing. The context-aware expert routing strategy aims to employ the impressive tool-use capacity of LLM to select vision experts with strong relevance to the user's image and instruction from a model pool. Specifically, we perform the context-aware expert routing in three steps during inference. First, the input image, user questions, and descriptions of expert models are converted into appropriate instructions that prompt the MLLM to perform expert selection. An example of the prompt instruction input and selection output is shown in Table 2. Such a routing task does not require image details and high-resolution input images, hence we directly downsample the base encoder's visual feature to obtain a coarse image embedding ( e.g. , 144 image tokens). The downsampled image tokens and instruction tokens are then fed to the LLM as inputs. Finally, the LLM generates the output text and we parse it to determine which vision expert should be selected for fine-grained knowledge extraction in the second stage. For instance, as depicted in Table 2, the LLM directly outputs the option's letter of DINOv2 and Pix2Struct, thus we only utilize them for the subsequent extraction. During training, we do not perform context-aware expert routing and replace the routing outputs with our routing annotations to improve efficiency.

Routing Data Construction. Compared with other MLLMs, MoVA requires additional routing annotations. We first introduce the formal definition of the data structure for an unambiguous understanding of the routing data. The data structure for expert routing introduces additional routing annotation R to the conventional multimodal data ( I , Q , A ) . Here, I represents the image, Q and A refer to the question-answer pair, and R refers to the expert set which contains the most appropriate ones to solve this question. Then the construction process for routing data can be formulated as ( I , Q , A ) →R , with the primary objective being to derive vision experts that optimally align with the sample ( I , Q , A ) . Intuitively, the language modeling loss can serve as an effective metric for evaluating how a data sample aligns with the vision expert. Specifically, we can reuse the LLaV A-1.57B models with various vision encoders presented in Section 1 to perform loss computation. Here, we denote the model with the base encoder as M 0 and the model with j -th expert among N experts as

Routing Prompt Input

You are a helpful assistant router. Based on the visual content, questions, and model pool the user provides, you need to consider the expertise of these models to select the most 3 suitable models to help you answer the questions. Answer with the model's letter from the given choices directly. If no models are selected, just answer 'none'.

Model pool:

<DINOv2 model description>

<Co-DETR model description>

<SAM model description>

<Pix2Struct model description>

<Deplot model description>

<Vary model description>

<BiomedCLIP model description>

Question:

Where is the red sign and what does it say?

Routing Prompt Output

A, D

M j . For the i -th sample ( I i , Q i , A i ) , we send it to models {M j | j ∈ { 0 , 1 , . . . , N }} and calculate the language modeling loss {L i,j | j ∈ { 0 , 1 , . . . , N }} . The j -th expert is regarded as a useful expert for the i -th sample only if L i,j < L i, 0 and will be added to the routing set R i . Note that we only keep up to 3 vision experts to avoid computation costs brought by too many additional experts. All the routing annotations of our training data are generated offline. We can directly parse and input these offline results to the subsequent expert fusion component during training.

Routing Data Augmentation. To preserve the expert routing robustness and generalization ability in open scenarios, we only randomly select 2K samples for training, remove the model name in model description, and rewrite the model descriptions using ChatGPT [54] for each expert. We also shuffle the model pool and randomly truncate the model pool during training.

3.3 Fine-grained Expert Fusion with MoV-Adapter

Wepropose the MoV-Adapter to facilitate fine-grained expert representation extraction and integration based on multimodal context. As shown in Figure 2, the MoV-Adapter consists of L adapter blocks and a text encoder. Each block contains an expert knowledge extractor, a dynamic gating network, and a transformer block. For the i -th block, the input feature is denoted as X i ∈ R C × H × W and we take the CLIP base encoder feature X ∈ R C × H × W as the input feature X 1 of the first block. We use G to indicate the indices of chosen K experts. The expert feature set is {F j | j ∈ G } . The final output feature of L adapter blocks is X L +1 . Additionally, we apply two residual blocks [55] with an average pooling to X L +1 to obtain a coarser image feature X L +1 out ∈ R C × H 2 × W 2 , which is further connected to the LLM text embedding space by an MLP layer.

Text Encoder. We introduce a pre-trained BERT as the text encoder to extract language context information from the user's instruction. We take the [CLS] token from the output of the text encoder as the text token X T ∈ R C T . It is worth noting that all the adapter blocks share the same text token.

Expert Knowledge Extractor. We adopt N cross-attention layers as the expert knowledge extractor to achieve efficient knowledge extraction. Note that only the expert features {F j | j ∈ G } and their corresponding cross-attention layers are involved in the extraction. For each selected expert feature F j ∈ R C j × H j × W j , we first align its resolution to X i with bilinear interpolation:

Image

Base Encoder

MoV-Adapter

Vision Experts

Routing

Annotation

User

Instruction

Answer

Supervision

Stage 1: Pretraining

Image

Base Encoder

MoV-Adapter

Vision Experts

Routing

Annotation

User

Instruction

Answer

Supervision

Stage 2: Supervised Finetuning

For the i -th MoV-Adapter block and the j -th cross-attention layer, we take input feature X i as query, and the aligned expert feature ˆ F j as the key and value:

Dynamic Gating Network. We employ a dynamic gating network to contribute to a fine-grained knowledge integration process for the conditional representation { Y i j | j ∈ G } . It is implemented with the softmax over the logits of an MLP layer, processing multimodal representation to generate expert-wise soft weight P i ∈ R K for the output of each cross-attention layer in the extractor. Specifically, the input to the gating network is the concatenated vector of a visual token X i V ∈ R C and the text token X T ∈ R C T . We obtain X i V with a global average pooling operation to X i . Then we concatenate them to compute the gating weights and the expert-wise outputs by computing the weighted sum:

Cross

Attention

Dynamic

Gating

& ℱ

!

…

Cross

Attention

& ℱ

"

Cross

Attention

& ℱ

#

Expert Knowledge Extractor

Self

Attention

FFN

(

)

$

Transformer

Block

)

$

)

$%!

Adapter

Block

Text

Encoder

×"

*

$

Global

Pooling

where P i j ∈ (0 , 1) is the soft weight for the j -th expert in the i -th block.

Transformer Block. The transformer block in the adapter block follows the vanilla design, consisting of a self-attention layer and an FFN layer. Taking the fused visual representation ˆ X i , its output will serve as the input feature X i +1 for the next adapter block.

3.4 Training Paradigm

As shown in Figure 3, the training process of MoV A consists of pretraining and supervised finetuning.

Pretraining. To improve multimodal generalization, we first construct 15M visual instruction samples across diverse domains as the training data: (i) Image caption data that covers 4M randomly selected samples from DataComp-1B [56], ShareGPT4V-PT [52], and ALLaVA-4V [57]. (ii) Visual grounding and localization dataset that encompasses Objects365 [58], RefCOCO [21], VisualGenome [59], PointQA [60], and Flickr30K [61]. (iii) Chart understanding data that includes MMC-Instruction [62], Chart2Text [63], DVQA [64], and SciGraphQA [65]. (iv) Text recognition and document parsing data that covers LLaVAR-PT [66] and 3M English document images from Common Crawl 4 . (v) LLaVAMed [67] for biomedical image understanding. During the pretraining phase, we only optimize the MoV-Adapter along with the base vision encoder while preserving the capabilities of the initial large language model. Meanwhile, we leverage the routing annotations generated via the method proposed in Section 3.2 to choose experts and ignore representations from irrelevant ones during training.

Supervised Finetuning. We utilize high-quality visual instruction tuning data that build upon LLaVA-665K [28] for finetuning. Additionally, we integrate several visual question answering

datasets across various domains, such as DocVQA [17], ChartQA [18], InfographicVQA [68], AI2D [69], ST-VQA [70], TextVQA [71], SynthDoG-en [72], Geometry3K [73], PGPS9K [74], Geo170K [75], RefCOCO, LLaVA-Med, VQA-RAD [76], and SLAKE [22]. We also encompass equivalent comprehensive captions [52, 57, 77, 78] generated by the advanced GPT4-V [54] for improved world knowledge. Apart from the above instruction tuning data, we convert the selected 2K routing annotations to instructions and incorporate them into the training data. In the supervised finetuning stage, only task-specific vision experts are frozen and we jointly optimize other components. The objective of supervised fine-tuning is to align the visual representation and the embedding of LLM, boosting its visual instruction-following capabilities.

4 Experiments

4.1 Implementation Details

As mentioned in Section 3.4, our training pipeline consists of two stages. In the pretraining stage, we use the AdamW optimizer with an initial learning rate of 2 × 10 -4 , a batch size of 1024, and train the model for 1 epoch. We jointly finetune the weights of all components except additional vision experts with a batch size of 128 and an initial learning rate of 2 × 10 -5 during supervised finetuning. We use 3 transformer blocks ( L =3 ) in the MoV-Adapter and its hidden dimension is 1024, which is consistent with the base vision encoder CLIP. The input resolution of the base vision encoder is 672 × 672. Two residual blocks with an average pooling are employed in the MoV-Adapter to reduce the number of output image tokens from 2304 to 576. For the experiment performed in Table 1, we follow the default setting of LLaVA-1.5 but incorporate several additional datasets, including DocVQA [17], ChartQA [18], RefCOCO [21], LLaVA-Med [67], VQA-RAD [76], and SLAKE [22]. More details about vision experts, ablations, and analysis are available in Appendix A.1 and A.3.

4.2 MLLMBenchmarks

We empirically analyze the multimodal capacity and generalization ability of MoVA on a wide range of challenging MLLM benchmarks in Table 3. This comprehensive assessment is conducted on MME [82], MMBench [16], QBench [83], MathVista [84], MathVerse [85], and POPE [20]. Compared to other open-source MLLMs with similar model complexity, MoVA with Vicuna-7B achieves the best performance across 7 MLLM benchmarks while offering a more favorable balance between training efficiency and performance. For instance, MoVA-7B surpasses the recent state-ofthe-art LLaVA-NeXT-7B [81] with a dynamic high resolution design, processing only 20% image tokens. Furthermore, we adopt Hermes-Yi-34B [86] as the LLM to validate the scaling property of MoVA. As depicted in Table 3, the performance of MoVA-34B is on par with popular proprietary

MLLMs ( e.g. , Gemini-Pro [79]) and outperforms Qwen-VL-Plus [6] on 5 MLLM benchmarks. For example, MoVA establishes new records on MMBench and MMBench-CN, even surpassing the GPT-4V [54]. These results suggest that the ensemble of vision experts with adaptive expert routing can serve as an effective dimension for MLLM model scaling.

4.3 Visual Question Answering

The evaluation results on VQA benchmarks are outlined in Table 4. We divide these benchmarks into general VQA benchmarks [89, 19, 90] and text-oriented VQA benchmarks [71, 18, 17, 69]. Thanks to the dynamic and efficient task-specific knowledge extraction, MoVA achieves state-ofthe-art performances across diverse VQA benchmarks. For general VQA benchmarks, MoVA-7B outperforms SPHINX-2k [4] equipped with Vicuna-13B on VQAv2 [89] and GQA by 4.2% and 1.9%, respectively. Besides, MoVA shows its proficiency in text recognition in various scenarios, encompassing scene text, chart, document, and diagram. For instance, MoVA-7B catches up to the current state-of-the-art generalist CogAgent [87] with 18 billion parameters on these text-oriented benchmarks with smaller model size. The MoVA model with 38B parameters even surpasses the wellestablished specialist model PALI-X-55B [88] by clear margins. These outstanding performances demonstrate MoVA's robust generalization capabilities across diverse domains.

4.4 Visual Grounding

We conduct experiments on Referring Expression Comprehension (REC) benchmarks [21] to evaluate the visual grounding ability of MoVA. The results are presented in Table5. The performance of

Model

LLaVA-Med

LLaVA-1.5

MoVA

VQA-RAD

Open

Close

28.6

35.3

38.3

56.3

68.9

68.9

SLAKE

Open

Close

70.6

73.1

78.2

54.6

63.7

68.8

LLaVA-Med (ft)

61.5

84.2

83.1

85.3

K

GQA

ChartQA

Dynamic

1

2

64.8

64.0

63.5

68.3

64.9

66.7

3

63.2

67.4

MoVA-7B is on par with the state-of-the-art specialist models that are elaborately designed for grounding tasks. For example, MoVA-7B achieves a score of 90.22% on RefCOCO+ val, which is 2.46% higher than the score of UNINEXT-H [93]. Our largest model MoVA-34B further pushes the performance bound of visual grounding on these benchmarks. These impressive results demonstrate MoVA's remarkable visual grounding capacity.

4.5 Medical Visual Question Answering

This experiment is conducted on popular medical VQA benchmarks VQA-RAD and SLAKE. We directly leverage the medical VQA evaluation metric adopted by LLaVA-Med. Each sample of VQA-RAD and SLAKE is observed only once during the training process of MoVA and LLaVA-1.5. For a fair comparison, we compare MoVA with the LLaVA-Med variant that is finetuned with only 1 epoch on the benchmark. The performance of the LLaVA-Med specialist that is fully finetuned on downstream tasks is also reported. As presented in Table 6, MoVA-7B consistently yields higher scores than LLaVA-Med and LLaVA-1.5, exhibiting its medical visual chat ability.

4.6 Ablation Study

Component-wise analysis. As presented in Table 7, we perform an ablation to thoroughly delve into the effect of each component. First, we try to replace the context-aware routing with random routing. Without task-relevant vision experts, the performance drops by a large margin, especially on the textoriented VQA benchmarks. Removing context-aware routing to leverage all vision experts also leads to similar results. It proves that both these modifications introduce biased information from irrelevant vision experts due to the removal of context-aware routing. Then, we ablate the effectiveness of the MoV-Adapter by replacing it with simple linear layers. The removal of fine-grained expert feature fusion downgrades performance across all datasets. These results delineate that each component in MoVA can consistently yield significant gains.

Number of activated experts. In the context-aware routing phase, the number of activated experts K is dynamic. We compare such a data-dependent design with other variations of constant K in this experiment. As presented in Table 8, the overall performance of dynamic K consistently outperforms other models with constant K . This reveals this dynamic implementation can fully exploit the task-specific knowledge of relevant experts while avoiding the incorporation of biased information.

Criteria for choosing better experts. To reduce the costs, our method only adopts ( N 1 ) models with N various encoders to identify the better vision expert separately . However, we do not explicitly consider the combination of the chosen vision experts. In this experiment, we compare our method with another strategy that considers vision encoders combination and enumerates ∑ 3 i =1 ( N i ) models for routing data construction. Specifically, we set N = 4 and employ a smaller model pool of DINOv2, Co-DETR, Pix2Struct, and Deplot to reduce training costs. As shown in Table 9, our method achieves comparable performance while requiring much less models for data construction.

Expert routing in open scenarios. We develop 105 human-verified testing samples that should be answered using novel experts for the expert routing task. These novel experts encompass 7 vision models [94, 72, 95, 92, 96, 55, 97] on various computer vision tasks and each expert corresponds to 15 evaluation samples. We manually check the correctness of the expert routing result. As presented in Table 10, a lightweight network, such as a MLP classifier fails to generalize to this open-world setting. Besides, increasing the routing training samples and removing data augmentations also lead to severe performance degradation. The results demonstrate our coarse-grained context-aware routing preserves the generalization ability for expert routing in open scenarios.

Adapter Design. In this section, we conduct ablation studies on the design of the MoV-Adapter. As presented in Table 11, we compared the impact of using 2, 3, and 4 adapter blocks on the model's performance. We observed that the baseline with 3 blocks can achieve better performance than other settings. Then, we substituted our multimodal gating for uniform gating to investigate its effectiveness. Each of the experts is assigned the same soft weight in the uniform gating. We find uniform gating brings consistent performance drops in the test benchmarks. It indicates that the lack of the dynamic soft-weight harms the overall performance since it fails to perform precise knowledge extraction.

Inference Analysis. As illustrated in Figure 1, MoV A consists of two stages: coarse-grained contextware expert routing and fine-grained expert fusion with MoV-Adapter. This two-stage inference pipeline can be further broken down into five steps: (i) Data preprocessing. We first process the input image with image processors and convert the input text into a token sequence with the LLM tokenizer. (ii) Base encoder forward. We extract the base image feature using the base CLIP encoder. Note that we only run the base encoder once since its output feature can be preserved and reused in the fourth step. (iii) LLM routing generation. We compress the base image features into 144 image tokens. The LLM generates a concise routing answer based on the compressed image feature and routing instruction. Vision experts and MoV-Adapter forward. (iv) According to the multimodal context and routing results generated in the previous step, we fuse vision features of the base encoder and activated experts in a coarse-to-fine manner. (v) LLM response generation. The LLM generates the final response given the fused vision features and user instructions. To investigate the inference efficiency of each step, we randomly select 200 images from the COCO val2017 dataset and adopt the common image caption instruction: Describe this image. The temperature for generation is 0. The latency is measured using bfloat16 and flash-attention 2 on an A100 80G GPU. We present the average inference latency of each step and show the average sequence length of the routing output and final response. The inference latencies for each step are 0.19s, 0.05s, 0.14s, 0.07s, and 10.24s, respectively. The average length of the routing output is 3.24 tokens, while the average length of the final response is 405.06 tokens. Compared to the LLM response generation (Step 5), the LLM expert routing (Step 3) generates much fewer output tokens and its latency is negligible (0.14s v.s. 10.24s). Therefore, our method does not bring significant inference costs.

5 Conclusion

In this paper, we reveal that the inherent bias of each vision encoder can diminish its generalization ability across other irrelevant domains by analyzing the performance of individual vision encoders versus the plain fusion of multiple encoders across various tasks. To deal with the problem, we propose MoVA, a powerful MLLM composed of coarse-grained context-aware expert routing and fine-grained expert fusion with MoV-Adapter. Based on multimodal context and model expertise, MoVA fully leverages representation from multiple context-relevant vision encoder experts flexibly and effectively while avoiding biased information brought by irrelevant experts. MoV A can achieve significant performance gains over current state-of-the-art methods in a wide range of benchmarks.

Limitations. We acknowledge some limitations in our paper that require attention. One limitation is the hallucination, which refers to the generation of text that appears plausible or coherent but is factually incorrect and misleading. This issue potentially presents in all powerful MLLMs. Additionally, the performance may be affected by failure cases of the context-relevant vision experts, leading to potential degradation. We plan to explore solutions for these limitations in future works.

Acknowledgments and Disclosure of Funding

The work was supported by the National Key R & D Program of China under Grant 2021ZD0201300.

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A Appendix / supplemental material

A.1 Vision Experts

Model Configuration. We present the detailed model configurations of our task-specific vision experts in 12. We adopt the official checkpoint weights that are publicly available.

Model Description. The model descriptions used in the routing prompt are released in Table 17.

A.2 Training Data Details

The training process of MoVA consists of two stages. In the Appendix, we present the training datasets with corresponding task templates of the first stage in Table 13. For the training data of the second stage, we follow the prompt format of LLaVA-1.5 [28]. The MoVA models with Vicuna-7B and LLama3-8B are pretrained using 64 A100 80G GPUs for 2 days, and finetuned using 32 A100 80G GPUs for 1 day. The MoVA with 34B LLM is pretrained using 128 A100 80G GPUs for 5 days and finetuned using 64 A100 80G GPUs for 2 days.

Table 15: Results of various vision encoder combination for routing.

Table 16: Effects of routing image tokens.

A.3 More Experiments

Image segmentation. In this experiment, we aim to investigate if task-specific knowledge can improve MoVA on the segmentation task. Therefore, we introduce a simple design to extend MoVA to segmentation tasks. Unlike segmentation generalists [98] that adopt an additional pixel decoder with high-resolution images for high-quality mask generation, we just formulate the referring segmentation task as sequential polygon generation [99]. We finetune MoVA and the baseline with a SAM-Huge [24] backbone on the RefCOCO referring segmentation datasets. MoVA achieves 57.1% gIoU on the testA benchmark, which is 2.6% higher than the 54.5% of baseline. This result indicates that MoVA is capable of exploiting task-specific knowledge to solve segmentation tasks.

Effect of LLM for expert routing. In this experiment, we investigate the effect of the LLM in our coarse-grained expert routing. As presented in Table 14, expert routing with LLM achieves the best performance. When the LLM is substituted for a lightweight MLP classifier and a BERT encoder, we need to increase the number of routing training samples from 2K to 1.6M to preserve model performance. Besides, both MLP classifier and BERT encoder fail to perform expert routing in open scenarios as stated in Table 10. Therefore, the strong tool-use capacity and generalization ability of LLM is critical to our flexible and effective expert routing.

Vision encoder for expert routing. In the coarse-grained expert routing, we only adopt the image feature of base vision encoder CLIP. As presented in Table 15, the method with CLIP achieves slightly better performance than other methods since such a routing task does not require elaborate expert knowledge. Besides, incorporating other vision experts with plain fusion also brings biased information and increases cost. To achieve a better trade-off between efficiency and performance, we only use CLIP for coarse-grained expert routing.

Number of image tokens in expert routing. Weablate the number of image tokens used in the expert routing stage. As shown in Table 16, routing with 144 tokens can achieve comparable performance to methods with more tokens. Considering the additional cost brought by processing more image tokens, we only use 144 tokens for routing.

Data construction. In this experiment, we analyze the effectiveness of our routing data construction method. The test split is constructed by randomly selecting 500 samples from the routing data. We first prompt GPT4-V [54] to determine whether the expert routing result is reasonable or not. These evaluation results are then manually checked and we finally compute the routing accuracy using the human-verified evaluation results. Our loss-driven data construction approach achieves 94.6% accuracy (473 samples are regarded as 'reasonable' by GPT4-V), demonstrating its effectiveness.

A.4 Qualitative Results

We apply MoVA to a wide range of real-world understanding and reasoning tasks to investigate its multimodal comprehension capacity. As presented in Figure 4 and Figure 5, MoVA successfully solves these diverse and complex cases thanks to the context-relevant expert knowledge.

A.5 Potential Societal Impacts

Positive impacts. The positive impact is to use MoV A to solve various open-world tasks in real-world scenarios. For example, MoVA can serve as a useful assistant to help people improve work efficiency and answer their questions.

Negative impacts. The potential negative social impact is to use MoV A model to generate misleading and false contents. This issue potentially presents in all multimodal large language models. We will try to improve its safety in future works.

Multimodal Commonsense

User:

What is the person with the highest detection confidence

score doing?

MoVA:

The person with the highest detection confidence score,

labeled "person 0.89", is eating ice cream

. She is holding a

white container

, and she appears to be

taking a bite of the

ice cream

. She is wearing a light pink shirt with

a red logo

on the left chest area

, black shorts with a white stripe on

the side, and white sneakers with black accents. Her hair is

styled in a bob cut, and she is wearing

a pair of white

earphones around her neck

. The person is standing with one

hand slightly raised, possibly to hold the container steady

while eating. The image captures her in a candid moment,

enjoying her snack while walking or standing outdoors.

User:

What are all the scene text in the image?

MoVA:

The text on the road sign reads:

-

"

CLEMENCEAU AVE

" at the

top

of the sign.

-

"

River Valley Rd

" with an arrow

pointing

right

.

-

"

" with an arrow

pointing left

.

-

"

Rd

" with an arrow

pointing left

.

Scene Text Recognition

CLEMENCEAU AVE

River Valley Rd →

Joke and Meme

Studying online

Zoom

Teacher

Wificompany

Students put

HAPO

UI Notification Understanding

11:18 AM

User:

Can you explain this meme?

MoVA:

The image is a humorous meme that compares the experiences of different parties involved

in online learning during the COVID-19 pandemic

. The meme is divided into four panels, each representing a different entity:

Zoom, the Wifi company, the teacher, and the

students

.

In the first panel, a man is shown throwing money into the air, symbolizing the

financial success of Zoom, a popular video conferencing platform that has seen a surge in usage during the pandemic

. The text "Zoom" is written above the image.

The second panel shows a man lying on a couch, holding a laptop, with a relaxed and content expression on his face. This represents

from home and enjoy a more flexible lifestyle image.

In the third panel, a man is shown throwing money into the air, similar to the first panel, but this time the text "Wifi

company" is written above the image. This suggests that

the companies providing internet services have also benefited from the increased demand for online services during the pandemic.

The fourth and final panel shows a clown with a sad expression, standing in front of a mirror with the words

"PUT ON A HAPPY FACE"

written on it. This represents the students

who have had to adapt to online learning and may be feeling overwhelmed or stressed text "Students" is written above the image.

Overall, the meme humorously highlights the different experiences and impacts of online

learning during the pandemic

, with Zoom and the Wifi company profiting, teachers

enjoying a more relaxed lifestyle, and students feeling the pressure to adapt to a new learning environment.

User:

Imagine that you are a robot operating a computer.

Like how humans operate the computer, you can move the mouse, click an icon with the mouse, or type

some texts with the keyboard.

Below is what you see on the computer screen.

Describe the image and predict your next move.

If the action involves moving the mouse, describe the location and the icon as detailed as possible.

MoVA:

This is a screenshot of a computer desktop with various icons and a window open. The desktop displays an image of a bridge over a body of water,

possibly a sea or ocean. The bridge is a long, straight structure connecting two land masses. The water is a deep blue, and the land is rocky with some

greenery.

The window also shows some text at the bottom that reads "Kevin Lin is calling you,"

suggesting that someone named Kevin Lin is trying to reach the user. The desktop has several icons, including one for the browser, a

folder, and some other applications. The taskbar at the bottom of the screen shows

the time as 11:18 AM and the date as 7/21/2023.

There are also icons for various applications, including a search bar and a folder.

As a robot, I would not have personal preferences or needs, so my next move would depend on the instructions given to me. If I were asked to open a new

tab in the browser, I would click on the browser icon in the taskbar.

If I

were asked to answer the call from Kevin Lin, I would click on the phone icon in the notification area of the taskbar.

If I were asked to open a specific application, I would click on the corresponding icon on the desktop. If I

were asked to search for something, I would click on the search bar icon in the taskbar.

7/21/2023

. The the teachers who have been able to work

. The text "Teacher" is written above the

Visual Referring Prompting

User:

What does this part of the poster highlights?

MoVA:

This part of the poster highlights the

"ReCo: Region-Controlled

Text-to-Image Generation" framework

, which is a system designed

to generate images from text descriptions. The poster outlines

the framework's components and benefits, such as the ability to

generate images with

precise region control

,

improved semantic

correctness

, and

better image quality

. It also emphasizes the

flexibility of the box tokens and the ease of use

, suggesting

that the system can create images that are both visually

appealing and semantically accurate.

Table Understanding and Reasoning

User:

In the DocVQA

task, how many points is the model with the highest

score higher than the model with the lowest score?

MoVA:

The model with

the

highest score in the DocVQA

task is "Pix2Struct

large"

with a score of

76.6

, while the model with

the lowest score

is "Dessurt" with a score of 63.2

. The difference in scores is

76.6 -

63.2 = 13.4 points.

ReCo:Region-Controlled Text-to-lmage Generation

CVPR

Text* in T2i Ger

description

Image-levell

text

global and

regional

Grounded

descripions

Box loken:keep ed oken fout

unded description in T21 generation

oription: controllablity and befte

Box Iokens: fexible to wrilte, as texd words

nded: seamless mix of text and box tokens

uracy: DETR AP.Rx

T21 pre-training: easy exensions

Grounded Region-Controlled

Text Inputs

nefening with textualized tkens

PaintSiil

toTectmess: +20% on

: beter FIO and

ibiity: spatial

SceneFID

Model

Param FT

TrainTS

VQAVQA Form

Doc

InfoDeep

KLC

TextVQATextCaps

MRC

Visual

一

Pix2Struclerge

Dessurt

Pix2Structoese

Donut

282M

1.3B

127M

176M

63.2

67.5

72.1

76.6

40.0

38.2

11.6

61.6

30.0

18.8

54.6

56.0

58.6

41.8

43.5

74.4

88.0

95.5

·

166

UReader

86M

×| 65.4

42.2

49.5

32.8

29.467.6

|59.3

| 57.6

118.4

221.7

Flow Chart Understanding

User:

Can you translate the flowchart to a C++ code?

MoVA:

Sure, here's a possible C++ code that implements the logic of the flowchart:

'''

#include <iostream>

int main() {

int num1, num2;

std::cout

<< "Input num1: ";

std::cin

>> num1;

std::cout

<< "Input num2: ";

std::cin

>> num2;

if (num1 > num2) {

std::cout

<< "The larger number is: " << num1 << std::endl;

} else {

std::cout

<< "The larger number is: " << num2 << std::endl;

}

return 0;

}

'''

This code prompts the user to input two numbers, num1 and num2, and then compares

them using an if-else statement. If num1 is greater than num2, the code will

display num1; otherwise, it will display num2.

Start

Input

num1

Input

num2

True

num1>num2

False

Display

Display

num2

num 1

Stop

Dense Text Recognition

User:

Please recognize the text in the image.

MoVA:

Abstract

As the key component in multimodal large language models (MLLMs), the ability of the

visual encoder greatly affects MLLMs' understanding on diverse image content.

Although some large-scale pretrained vision encoders such as vision encoders in CLIP

and DINOv2 have brought promising performance, we found that there is still no

single vision encoder that can dominate various image content understanding, e.g.,

the CLIP vision encoder leads to outstanding results on general image understanding

but poor performance on document or chart content. To alleviate the bias of CLIP

vision encoder, we first delve into the inherent behavior of different pre-trained

vision encoders and then propose the MoVA, a powerful and novel MLLM, adaptively

routing and fusing task-specific vision experts with a coarse-to-fine mechanism. In

the coarse-grained stage, we design a context-aware expert routing strategy to

dynamically select the most suitable vision experts according to the user

instruction, input image, and expertise of vision experts. This benefits from the

powerful model function understanding ability of the large language model (LLM). In

the fine-grained stage, we elaborately conduct the mixture-of-vision-expert adapter

(MoV-Adapter) to extract and fuse task-specific knowledge from various experts. This

coarse-to-fine paradigm effectively leverages representations from experts based on

multimodal context and model expertise, further enhancing the generalization ability.

We conduct extensive experiments to evaluate the effectiveness of the proposed

approach. Without bells and whistles, MoVA

can achieve significant performance gains

over current state-of-the-art methods in a wide range of challenging multimodal

benchmarks.

Abstract

of the visual encoder greatly affects MLLM's understanding on diverse image

content. Although some large-scale pretrained vision encoders such as vision

encoders in CLIP and DINOv2 have brought promising performance, we found

that there is still no single vision encoder that can dominate various image content

understanding,e.g.,the CLIP vision encoder leads to outstanding results on general

image understanding but poor performance on document or chart content.To

alleviate the bias of CLIPvision encoder,we first delve into the inherent behavior

of different pre-trained vision encoders and then propose the MoVA, a powerful

and novel MLLM, adaptively routing and fusing task-specific vision experts with

a coarse-to-fine mechanism.In the coarse-grained stage, we design a context-

aware expert routing strategy to dynamically select the most suitable vision experts

according to the user instruction,input image,and expertise of vision experts.

This benefits from the powerful model function understanding ability of the large

language model (LLM).In the fine-grained stage,we elaborately conduct the

representations from experts based on multimodal context and model expertise,

to evaluate the effectiveness of the proposed approach. Without any bells and

mixture-of-vision-expert adapter (MoV-Adapter) to extract and fuse task-specific

knowledge from various experts. This coarse-to-fine paradigm effectively leverages

further enhancing the generalization ability.We conduct extensive experiments

whistles,MoVA can achieve significant performance gains over current state-of-

the-art methods in a wide range of challenging multimodal benchmarks.

Table 17: Model descriptions used in the context-aware expert routing. We describe the pros and cons of each expert model in the routing prompt. We only present 3 captions for each expert here.

DINOv2 description

(1) This model demonstrates exceptional prediction capabilities across a range of image-related tasks, including image classification, object detection, segmentation, and image retrieval. The model leverages advanced self-supervised learning techniques to achieve high performance without relying heavily on labeled data.

(2) This model shows very strong prediction capabilities on tasks such as image classification, detection, segmentation, and image retrieval. However, it encounters challenges in accurately reading text within images.

(3) This model can effectively extract the accurate spatial and semantic information from natural images.

Co-DETR description

(1) This model is a state-of-the-art object detector pretrained on natural images. It can enable models to solve object-centric problems. Nonetheless, this model struggles with processing background elements in natural scenes.

(2) This model is a cutting-edge object detection model that can accurately detect objects in images. However, it struggles with identifying text in images.

(3) This model is a state-of-the-art object detector that can identify objects in images.

SAM description

(1) This model is an image segmentation model. This model can segment the precise location of either specific objects in an image or every object in an image.

(2) This model is a leading image segmentation framework and achieves strong zero-shot segmentation performance.

(3) This model is a promotable segmentation system with zero-shot generalization to unfamiliar objects and images.

Pix2Struct description

(1) This model excels in text recognition, achieving state-of-the-art text analysis results across distinct domains: documents, illustrations, user interfaces, natural images containing text, and images of charts.

(2) This model demonstrates exceptional proficiency in text recognition, delivering cutting-edge text analysis performance across various domains.

(3) This model can automate the extraction of information from scanned documents, making it easier to digitize and manage large volumes of paperwork.

Deplot description

(1) This model is a specialized model designed to achieve state-of-the-art plot and chart understanding performance.

(2) This model is a fine-tuned version of an existing text recognition model. It has been specifically trained to achieve superior performance in plot and chart understanding tasks.

(3) This model can help detect the text within the input document, diagram, and chart images.

Vary description

(1) This model can achieve more fine-grained vision perception for images with text, such as document-level Chinese/English OCR, book image to markdown or LATEX, Chinese/English chart understanding.

(2) This model can handle images with text effectively and accurately, enabling advanced tasks such as document OCR and chart understanding.

(3) This model can accurately process images with text, enabling tasks such as OCR. However, it cannot process natural images without text.

BiomedCLIP description

(1) This model is a foundation model designed for biomedical vision-language processing.

(2) This model is capable of biomedical images, such as chest X-ray and radiology images.

(3) This model is a state-of-the-art biomedical vision-language model. It has been shown to achieve significant improvements in biomedical image-text tasks.

NeurIPS Paper Checklist

The checklist is designed to encourage best practices for responsible machine learning research, addressing issues of reproducibility, transparency, research ethics, and societal impact. Do not remove the checklist: The papers not including the checklist will be desk rejected. The checklist should follow the references and follow the (optional) supplemental material. The checklist does NOT count towards the page limit.

Please read the checklist guidelines carefully for information on how to answer these questions. For each question in the checklist:

You should answer [Yes] , [No] , or [NA] .

[NA] means either that the question is Not Applicable for that particular paper or the relevant information is Not Available.

Please provide a short (1-2 sentence) justification right after your answer (even for NA).

The checklist answers are an integral part of your paper submission. They are visible to the reviewers, area chairs, senior area chairs, and ethics reviewers. You will be asked to also include it (after eventual revisions) with the final version of your paper, and its final version will be published with the paper.

The reviewers of your paper will be asked to use the checklist as one of the factors in their evaluation. While "[Yes] " is generally preferable to "[No] ", it is perfectly acceptable to answer "[No] " provided a proper justification is given (e.g., "error bars are not reported because it would be too computationally expensive" or "we were unable to find the license for the dataset we used"). In general, answering "[No] " or "[NA] " is not grounds for rejection. While the questions are phrased in a binary way, we acknowledge that the true answer is often more nuanced, so please just use your best judgment and write a justification to elaborate. All supporting evidence can appear either in the main paper or the supplemental material, provided in appendix. If you answer [Yes] to a question, in the justification please point to the section(s) where related material for the question can be found.

IMPORTANT, please:

Delete this instruction block, but keep the section heading 'NeurIPS paper checklist" ,

Keep the checklist subsection headings, questions/answers and guidelines below.

Do not modify the questions and only use the provided macros for your answers .

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: We clarify our main claims and contribution in the abstract and introduction section.

Guidelines:

The answer NA means that the abstract and introduction do not include the claims made in the paper.

The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.

The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.

It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: We discuss the limitations of MoV A in the conclusion section.

Guidelines:

The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.

The authors are encouraged to create a separate "Limitations" section in their paper.

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3. Theory Assumptions and Proofs

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [NA]

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The answer NA means that the paper does not include theoretical results.

All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced.

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Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.

Theorems and Lemmas that the proof relies upon should be properly referenced.

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Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

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Justification: We fully release the implementation details and experiment settings in the Section Experiments and Section Appendix.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a MoVA to extract and fuse task-specific knowledge from different visual encoder. Besides, the paper designs a coarse-to-fine mechanism, which dynamically selects the most suitable vision experts, then extracts and fuses task-specific knowledge from various experts. The experimental results are encouraging and validate the performance of the proposed method.\n\nSTRENGTHS:\n1. This paper proposes a new perspective, which is to improve the multi-task reasoning ability of MLLMs based on the different perception ability of the different encoders. \n2. The paper is fluent and easy to understand.\n3. The extensive experiments evaluate the effectiveness of the proposed approach.\n\nWEAKNESSES:\n1. The number of components in line 102 does not match the serial numbers.\n2. During the inference phase, the process involves two rounds of LLM inference and two Base Encoder, which results in significant inference time and GPU memory usage. This poses a challenge for the practical application of MLLMs.\n3. This is a well-completed article, but I am concerned about its novelty. The method seems similar to the adapter+agent approach described by Wang et al. [1]. Could you explain the differences and advantages of your method compared to theirs?\n\n[1] Wang X, Zhuang B, Wu Q. Modaverse: Efficiently transforming modalities with LLMs. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 26606-26616.\n\nQUESTIONS:\nWhy is the last row of MoVA results in Table 1 different from those in Table 3? In other words, The caption of Table 1 shows that the same data was used to train each model. Does this mean that all models used LLaVA-1.5 data? If so, I think this table will inspire the community and I would like to ask if you can release the checkpoints of these models.\n\nLIMITATIONS:\n1. As mentioned in the conclusion, the performance may be affected by failure cases of the context-relevant vision experts, leading to potential degradation. Additionally, it is influenced by the choice of encoder results in the first stage of inference.\n2. The model's complex inference structure increases the inference burden.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-09"}

comments: SUMMARY:
This paper proposes MoVA, a powerful and novel MLLM, adaptively routing and fusing task-specific vision experts with a coarse-to-fine mechanism. MoVA first leverages the tool-use capabilities of LLM, routing the most appropriate experts from expert candidates and then uses the MoV-Adapter module to enhance the visual representation.

STRENGTHS:
1. The writing is easy to follow.
2. The idea of using LLM to route the most appropriate experts is interesting.

WEAKNESSES:
1. Is there an adapter structure in MoVA? Why do we call it MoV-Adapter?
2. The experiments use SFT data from different MLLMs should be aligned (the same). The comparison is not fair with different SFT data.
3. More vision encoder enhancement methods should be compared using the same SFT data, including

[1] VCoder: Versatile Vision Encoders for Multimodal Large Language Models.

[2] MoF (Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs)

[3] MouSi: Poly-Visual-Expert Vision-Language Models

[4] BRAVE: Broadening the visual encoding of vision-language models

4. The inference speed of LLM is very slow. I think MoVA using LLM as a router selection will decrease the speed of the whole process. How to solve this problem?

QUESTIONS:
In line 102, I just found four components, but the paper claims five.

LIMITATIONS:
The authors have adequately addressed the limitations.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 2
DATE: 2024-07-11

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper considers a critical issue in vision language models, i.e., the vision encoder design. The authors proposes MoVA, a coarse to fine routing strategy, adaptively routing and fusing task specific vision experts which best suit the task. Experiments conducted on a wide range of MLLM benchmark demonstrate the advantages of the proposed MoV adapter.\n\nSTRENGTHS:\n1.\tThe motivation is clear, and the method is easy to follow\n\n2.\tThe proposed coarse to fine routing strategy is reasonable\n\nWEAKNESSES:\n1.\tAs for OCR and related tasks, it has been validated that high resolution is much more import than model size, and image split like LLaVA Next, Monkey, Ferret etc. have been widely used. I wonder the importance of including task specific models. It looks redundancy, and importantly, we cannot include all models that cover different tasks exhaustively\n\n2.\tAs shown in Table 1, the main improvement results from the OCR related Doc and Chart task, which may resolved by current MLLMs baseline such as LLaVa Next, I wonder the  necessity of the using MoE experts, as other module, adding resolution is more reasonable. In Table 3, since the PT, SFT and pretrained models are different, it is hard to validate the gains are from the MoV experts.  \n\n3.\tDoes the method introduce error propagation, say, the first stage LLM routing is not correct, which may harm the performance of the whole framework. On the other hand, if the coarse routing is simple, could we say that this stage we do not need to carefully design, and a tiny LLM or even rule based according to the prompt is enough? Since current framework seems complex using two LLMs, the comparison is not fair, and I wonder if there is a better routing strategy.\n\nQUESTIONS:\nThe main concern is the rationality of using MoV on vision side, while two step routing is interesting, it adds the complexity (using two LLMs), and it is not clear the advantages of MoV over the latest MLLM benchmark.\n\nLIMITATIONS:\nNone", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-14"}

comments: SUMMARY:
In this paper, the authors propose MoVA, a powerful MLLM composed of coarse-grained context-aware expert routing and fine-grained expert fusion with MoV-Adapter. Based on multimodal context and model expertise, MoVA fully leverages representation from multiple context-relevant vision encoder experts flexibly and effectively while avoiding biased information brought by irrelevant experts. MoVA can achieve significant performance gains over current state-of-the-art methods in a wide range of benchmarks.

STRENGTHS:
1. The authors comprehensively investigate various vision encoders and analyze the performance of individual vision encoders versus the plain fusion of multiple encoders across various tasks.
2. The authors propose MoVA, a powerful MLLM composed of coarse-grained context-aware expert routing and fine-grained expert fusion with MoV-Adapter.
3. The proposed MoVA can achieve significant performance gains over state-of-the-art methods in a wide range of challenging benchmarks.

WEAKNESSES:
1. Despite the proposed MoVA in this paper achieving state-of-the-art performance across multiple tasks, leveraging multiple visual encoders in MLLM design does not introduce novelty. Additionally, the design of MoV-Adapter also lacks innovation.
2. This paper lacks a comprehensive evaluation regarding the routing selection of multiple visual encoders using LLMs, necessitating a more targeted and comprehensive assessment from the authors.

QUESTIONS:
Please refer to Weakness Section for more details.

LIMITATIONS:
The authors have clearly illustrated the weakness of this work by pointing out the hallucination and the model performance may be affected by failure cases of the context-relevant vision experts.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-14