Visual Perception by Large Language Model’s Weights
Authors: Feipeng Ma
Hongwei Xue
Yizhou Zhou
Guangting Wang
Fengyun Rao
Shilin Yan
Yueyi Zhang
Siying Wu
Mike Zheng Shou
Xiaoyan Sun
Venue: NeurIPS 2024 poster (2024)
Keywords: Multi-modal large language model
vision language pre-training
multi-modal learning
Primary area: generative_models

Abstract: Existing Multimodal Large Language Models (MLLMs) follow the paradigm that perceives visual information by aligning visual features with the input space of Large Language Models (LLMs) and concatenating visual tokens with text tokens to form a unified sequence input for LLMs. These methods demonstrate promising results on various vision-language tasks but are limited by the high computational effort due to the extended input sequence resulting from the involvement of visual tokens. In this paper, instead of input space alignment, we propose a novel parameter space alignment paradigm that represents visual information as model weights. For each input image, we use a vision encoder to extract visual features, convert features into perceptual weights, and merge the perceptual weights with LLM's weights. In this way, the input of LLM does not require visual tokens, which reduces the length of the input sequence and greatly improves efficiency. Following this paradigm, we propose VLoRA with the perceptual weights generator. The perceptual weights generator is designed to convert visual features to perceptual weights with low-rank property, exhibiting a form similar to LoRA. The experimental results show that our VLoRA achieves comparable performance on various benchmarks for MLLMs, while significantly reducing the computational costs for both training and inference. Code and models are released at \url{https://github.com/FeipengMa6/VLoRA}.

Visual Perception by Large Language Model's Weights
Feipeng Ma 22, Hongwei Xuel:2.3 Yizhou Zhou?; Guangting Wang', Fengyun Rao² Shilin Yan', Yueyi Zhang', Siying Wu', Mike Zheng Shou', Xiaoyan S.52
University of Science and Technology of China WeChat, Tencent Inc.
"Show Lab, National University of Singapore Institute of Artificial Intelligence, Hefei Comprehensive National Science Center {mafp, xuehongwei @maill ustc. ustc.edu. cn harryizzhouuttenccnt com, {zhyuey, sunxiaoyan) Custc. edu. en
*Fudan University
Abstract
Existing Multimodal Large Language Models (MLLMs) follow the paradigm that perceives visual information by aligning visual features with the input space of Large Language Models (LLMs) and concatenating visual tokens with text tokens to form a unified sequence input for LLMs. These methods demonstrate promising results on various vision-language tasks but are limited by the high computational effort due to the extended input sequence resulting from the involvement of vi- sual tokens. In this paper, instead of input space alignment, we propose a novel parameter space alignment paradigm that represents visual information model weights. For each input image, we use a vision encoder to extract visual features, convert features into perceptual weights, and merge the perceptual weights with LLM's weights. In this way, the input of LLM does not require visual tokens, which reduces the length of the input sequence and greatly improves efficiency. Following this paradigm, we propose VLoRA with the perceptual weights gen- erator. The perceptual weights generator is designed convert visual features to perceptual weights with low-rank property, exhibiting a form similar to LoRA. The experimental results show that VLoRA achieves comparable performance on various benchmarks for MILMM,, while significantly reducing the computa- tional costs for both training and inference. Code and models released com httmm//eieggg////// /VLoAA
Introduction
Large language models (LLMs)) [57, 65, 47] have achieved promising performance on most natural language tasks and have shown great generalization ability in solving real-world problems. Derived from LLMs, multimodal large language models (MLLMs) [36, 66, 4, 62, 55, 48] take a step toward artificial general intelligence (AGI) by perceiving visual information from the real world. Therefore, way of perceiving visual information the key to moving from LLM MILM.
To perceive visual information, recent MLLMs follow an input space alignment paradigm that aligns visual features with the input space of LLM and concatenates visual tokens with text tokens to form a unified sequence as input for LLM. For instance. LLaVA [36] uses CLIP- -iT-LL1-- [50] as the visual encoder and introduces linear projector align the visual tokens with the input space of LLM. Monkey [31] divides input images into uniform patches and equips individual adapters for each
'This work was performed while Feipeng Ma and Hongwei Xue were interns at WeChat, Tencent Inc. Project Leader.
*Corresponding authors.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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patch to handle high-resolution images. Recent work [56] also identifies the visual shortcomings of CLIP for MLLMs as "CLIP-blind pairs" and integrates vision self supervised learning features with MLLM to address this issue. DeepSeek-V.. [41] and Sphinx [32] also adopt hybrid vision encoders. Vary [58] identifies that a fixed vision vocabulary limits the dense and fine-grainer visual perception and introduces a new vocabulary to address this issue.
Despite these efforts to advance MLLM in visual perception, the paradigm of input space alignment remains unchanged, which can result in computational inefficiency for both training and inference. The computational cost of MLLM is concentrated on the attention mechanism of LLM, which (nn)) when the length of the input sequence is n. Using ViT-L-14 14 as the vision encoder, x24 low- resolution image can result in 256 visual tokens, and the length increases to 576 when the resolution slightly raises to 336x336. Considering high-resolution images, some works [32, 35, 31. split an image into multiple sub-images for capturing fine-grained information, leading to a significantly higher number of visual tokens. For instance, Sphinx-2k [32] adopts 2,890 visual tokens, while [11] even uses up to 8,737 visual tokens. Concatenating such a long sequence of visual tokens to text tokens results in a dramatic increase in computational overhead for both training and inference. Specifically, current MLLMs are usually pre-trained on web-crawled image-text pairs, which usually have very short texts, with an average word count of 10.95 for LAION- [1]. As a result, the number of visual tokens during the pre-
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2B [51] and 8.99 for LAION-COCK training stage is about 20 to 50 times the number of text tokens, which suggests that the involvement of visual tokens seriously affects the efficiency of the pre- pre-training. Some works [27, 9, 24] employ resamplers to reduce the number of visual tokens to a fixed count but still follow the input space alignment paradigm and introduce extra visual tokens for L.MMs.
To address this issue, we explore a novel parameter space alignment paradigm where visual infor- mation is represented as LLM's weights. As shown in Fig. 1, for an input image, we use a vision encoder to extract visual features. Then, the visual features are converted to perceptual weights, which represent visual information as model weights. The perceptual weights can be directly merged with LLM's weights. Thus, the visual information is merged into LLM in the form of weights, eliminating the need for visual tokens in the LLM's input and significantly improving efficiency. Building on this paradigm, introduce VLoRA. which contains the perceptual weights generator. The perceptual weight generator is designed to convert visual features to perceptual weights. LLMs usually contain a large number of parameters, for feasibility and efficiency, perceptual weights are designed with low-rank property. Thus the generated perceptual weights are similar to the form of LoRA weights.
Our contributions are summarised follows:
1. We explore novel paradigm for MILMs that aligns visual features with the parameter space of I.LMs, which highly improves the efficiency of MI.LMS
2. Based on this paradigm, we propose VLoRA and design the perceptual weights generator that generates low-rank perceptual weights.
3. Experimental results demonstrate the effectiveness and efficiency of our approach. We obtain results comparable to those of state-of-the thrt MLLMs on various benchmarks, including MMBench, ScienceQA, HallusionBench,, and MMMU.
2 Related Works
Multimodal Large Language Models. Current MILMS are developed from LLMs by aligning
visual features into the input space of LLMs. Many efforts have been made explore introducing visual perception capability for L.MMs. LLaVA [36] connects the visual encoder of CLIP to the
Vicuna [65] with a linear projector. Further research that follows this paradigm focuses on improving MLLMs from the perspective of vision encoder and projector DeepSeek- VL [41] use SigLip [61] to extract high-level semantic features and use SAM-B [22] to process low-level features. Tong et al. [56] finds that visually distinct images can be encoded as similar due to the shortcoming of CLIP and integrates vision self-supervised learning features with CLIP features. Sphinx [32] ensembles various vision backbones that have different architectures, pre-training paradigms, and information granularities. These works input the entire visual tokens sequence into the LLM,, which can lead to high computational cost during training and inference. Specifically. LLaVA [34] and DeepSeek- VL [41] utilize 576 visual tokens, Sphinx-20 [32] employs 2,890 visual tokens, and InternI.M- XComposer2-4KHHH [11] uses up to 8.737 tokens. Some works consider adopting cross- -ooss-attention

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Layer
Decoder Layer
Decoder Layer
Perceptual Weights
di W2
Generator
We
Decoder Layer
Decoder Layer
1
We
Ik
+
Vision Encoder
Decoder Layer
Decoder Layer
Parsepiuall Weights
Tovens
LLM Weights
Visual
How to make this dish?
How make this dish?
addition two weight
(a) Visual feature extractor
Input space alignment
(c) VLORA: Parameter space alignment
Figure I: Overview of the input space alignment and the parameter space alignment paradigms. The imput space alignment paradigm is aligning visual features with the input space of LLM and concatenating visual tokens with text tokens as input for LLM. Our proposed VLoRA follows the parameter space alignment paradigm that aligns visual features with the parameters of LLM and merges perceptual weights generated by the perceptual weights generator with LLM's weights.
architecture the projector to improve efficiency. MiniGPT4-v1 [66] and BLIP series [27. 9] adopt Q-Former as the projector, which reduces the length of visual tokens to a fixed number of 64. Qwen-VL [5] uses single-layer cross-attention module incorporated with 2D absolute positional encodings to avoid the potential loss of positional details. However, these improvements still follow the paradigm of aligning visual features the input space of LLM, introducing extra computational overhead on LLM inference. Different from previous work, our VLoRA aligns visual features with the parameter space of LLM. The visual information can be represented as perceptual weights in LoRA format and merged into LLM's weights during inference.
Errameter-Efficiee Fine- Tone-TTning. Parameter-efficient fine-tuning (PEFT) is a key technique for fine- tuning large pre-trained models, including LLMs and MLLMs. PEFT methods freeze the backbone and only fine-tune small number of parameters, which can be typically categorized into three classes: adapters [17, 49, 54, 63], prefix-tuning [29, 26, 38], and Low-Rank Adaption (LoRA) [18, 37, 10]. Houlsby al. [17] design bottleneck adapters and insert two adapters into the transformer layers, one after the attention module and one after the feed-forward network. LLaMA-Adapter [63] inserts learnable prompts into L of N decoder layers and uses zero- iaro-initialized attention for stable training. Prefix-tuning [29] prepends a set of learnable prefix vectors at the query and key of the self-attention module for every layer. Prompt-tuning proposes to only prepend learnable vectors the input prompt with no intermediate-layer prefixes. LoRA [18] uses learnable low-rank matrices to approximate the backbone's 's weight updates, and the low-rank matrices can be merged with the backbone during inference without extra inference burden. Considering the pre- pre-training stage, current MLLMs usually freeze the unimodal backbones and project visual tokens through a learnable projector, then prepend visual tokens into the input sequence of LLMs, which can be seen as prefix-tuning methods. Our VLoRA closer to the style of LoRA. Specifically, VLoRA generates low-rank perceptual weights, which can be seen as generated visual parameters matrix AWA Raxr multiplied with learnable matrix AWB B Rixh, Similar to LoRA, the perceptual weights can be injected into LLMs' weights without introducing extra inference overhead.
Hyperketwork. HyperNetwork is technique that employs one network generate the weights for another network. Heperrsrworks [16] proposes static hypternetwork for CNN and dynamic hypternetwor for RNN. HyperFormer [43] proposes hypterformer to generate adapter parameters for all layers and multiple tasks using shared hyptereetwors.. The parameter generation of both methods designed on task-level for pre-defined tasks. HyperPELT [64] employs a shared hypernetwork that generates weights for prefix-tuning and adapter-tuning modules. Mem VP [20] concatenates visual prompts with FFN weights to inject visual knowledge. contrast to HyperNetworks and HyperFormer, 1) VLoRA focuses on sample-level parameter generation, the generated LoRA weights are conditioned on the input image without pre-defining tasks during training. Since the goal of MILMS is to address wide range of tasks and problems that are difficult to fully define in advance, lesk- adaptation is unsuitable for MI.MMs. 2) VLoRA utilizes the generated parameters in LoRA way. Sample-level parameter generation can lead significant changes in model parameters.

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Add & Norm
Concat
forward
Scxted Dot- Product
Add & Norm
Atsenton
Self attentitnnion
W
W
W2
(8) Decoder Block of LLM
(b) Multihead neff-Attention
(c) Feed-forward Network
Figure 2: Details of the LLM Decoder Block. (a) illustrates the details of the LLM decoder block, including the multi-head self-attention module and the feed-forward network. (b) provides a detailed view of the multi-head self-attention module, which incorporates four types of weights: Wo. WK. K. Wv, and Wo. (c) depicts the feed-forward network, which consists of the weights Wi and W2.
VLoRA, adopting the LoRA method, can better maintain the inherent capability of the pre-trained LLM. Unlike HyperPELT and Mem VLoRA inject visual information any linear module, offering flexibility. 2) Unlike task-level PEFT methods, VLoRA is sample-level, generating weights for individual input images. Our evaluations, mainly in zero-shot settings, demonstrate VLoRA's strong generalization ability.
3 Method
3.1 Preliminaries
In this subsection, we review the details of the decoder block in the current LLM. As shown in Fig. 2, the decoder block of LLM contains self-attention module and feed-forward network.
Self-attention. As shown in Fig. (b), the self-attention module contains four types of linear layers: query Wq E gxx key WK E Rxxd value Wv 12 and output Wo E Rg Here, represents the dimension of the hidden states of LLM, and d represents the dimension of each attention head. For each input token I; E l in the input sequence X (I1, #2, ... IN). it is multiplied by linear layers Wo. WK. Wv, obtaining X = XWQ. X XW and X = XWy. Then, attention operation executed along the sequence dimension as follows:
X
Attention X x* x²) = softmax
)
(1)
Vd
The self-attention mechanism is performed on each head, and the outputs from different heads are concatenated and multiplied by output linear layer with weights Wo.
Feed-forward Network. As shown in Fig. 2 (c), the feed-forward network is an MLP with two fully connected layers and a non-linear activation function. The formulation can be written follows:
FFN(x;) W(r,Wi)W:
(2)
where x; is the input token, á is the activation function, and Wi and W2 are the weights of two fully connected layers. To summarize, the decoder block of LLM has five types weights, including Wo- WK. Wv, Wo from the self-attention module, and W1, W2 from the feed-forward network.
3.2 Visual Perception by LLM's Weights
Previous MLLMs follow the paradigm of aligning the visual features with the input space of LLM and require additional visual tokens as LLM's input, which can lead to computational inefficiency. multiple images as
This inefficiency becomes more pronounced when encountering high-resolution the number of tokens increases drastically. To address this issue, we propose to align visual features with LLM's parameter space without introducing extra tokens into L.M'' input.
To achieve this goal, we represent the visual information the input image as perceptual weights and integrate them into the weights of LLM. This approach allows LLM to perceive visual information

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Linear
Linear
Solf Attuntion
Cross Attortion
Feed Feed-Forwerd Forword
Shured Linear
X
Linear
Perceptual Queries
ER
AW2
Encoder
Visual
X
Visual Parameters
Perceptual Weights
LoRA:
AWA
(b) Equivalent form to LoRA, where AW, learnable
(a) Perceptual Weights Generator
Figure Perceptual Weights Generator. Figure (a) illustrates the pipeline of our perceptual weights generator. We set & learnable perceptual queries, which interact with image features in N decoder blocks, and obtain & visual parameters. Then, shared linear layer and &s independent linear layers are used to convert these visual parameters to perceptual weights AW. Figure (b) demonstrates that our approach is formally consistent with LoRA.
without introducing extra tokens into the input. As mentioned Sect. 3.1, LLM's decoder blocks have five types of weights. We use W E kxh to denote the weight matrix of LLM. For an input image I, we first adopt vision encoder f(-) to extract the visual features 2 f(J,, where Bexd. is the number of visual tokens, and de is the dimension of visual features. Then, we design perceptual weights generator g(-) convert the visual features perceptual weights AW R It is worth noting that, given that we want LLM to perceive visual information while preserving its language capabilities, AW is low-rank matrix, which also helps to reduce the computation cost of the perceptual weights generator. With the generated perceptual weights AW, we can directly merge into the LLM's weights as:
W W + AW.
By integrating the weights transferred from the visual features into the LLM's weights, the visual perception ability is naturally equipped. After merging the weights, extra inference burden will be introduced for LLM. For any weights in each decoder block LLM, we can generate the corresponding perceptual weights and integrate them into LLM's weights.
3.3 Perceptual Weights Generator
To convert visual features to perceptual weights AW E Rixh, we propose the perceptual weights generator. Since each layer and each type of weight in LLM focus on different visual information. our perceptual weights generator needs be able generate weights corresponding to each of the LLM weights flexibly.
Inspired by DETR [6] and BLIP-2 [27], design the perceptual weights generator decoder- only architecture with cross-attention layers to generate AW E RAxh As shown in Fig. (a), the perceptual weights generator contains N blocks, each comprising a self-attention module, cross-attention module, and a feed-forward network. The hidden states dimension of the perceptual weights generator is hp. where h, h h. We set & learnable perceptual quires corresponding to the number of decoder blocks where we want to insert perceptual weights. For each block, the perceptual queries first pass through the self-attention module, then interact with visual features the cross-atted tross-attention module, and finally go through a feed-forward network. After N blocks, we obtain features Pe, Rdy, The features Pe should AW E Rixh, However, due to hp < h- h. directly mapping the dimensions of the p. from hp h h with linear layer can introduce a large number of parameters, dramatically reducing the feasibility.. Therefore, we consider introducing the low-rank property in this process. We adopt shared linear layer W Wshare E h-r to map all features pa from hi as follows:
mapped the target shape perceptual weights
Wr = Pa W shorer
(4)
where is the rank for perceptual weights and W, e h is visual parameter.
5

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CFLOP with different number visual tobens
PLOPS ratio with different rumber of input visual tolees
Ludah (CHAal
C-256
(LIBA 20-2241
18
22-
300
1003
1203
3003 2590
590
1060 1900
2000 2506
Figure Comparison FLOPs. This figure shows the FLOPSs of LLaVA and VLoRA with different numbers of input visual tokens. The left subplot illustrates the change in GFLOPs, the right subplot plots the ratio of GFLOPs for VLoRA to LLaVA, and denotes the number of text tokens.
And we reshape the output Wa as h xr. When ascending to the target dimension h x independent linear layers W, E Rrxh are used for each visual parameter and obtain & perceptual weights AW, this process can be formulated as follows:
AW = W.W.,
(5)
Substituting Eq. (5) into Eq. (3), we get:
W = W + AW = W W.W.
(6)
Considering the low-rank property of W, and W., we can observe that Eq. (6) and LoRA [18] are of the same form, where W, corresponds AWA A and W, corresponds AWp B. As illustrated in Fig. (b), our perceptual weights generator can be seen as "LoRA weights generator" from the perspective of LoRA. This is because generates AWA and AWB for weights of LLM. Our perceptual weights generator generates one type of perceptual weights for k decoder blocks at a time. For generating multiple types of weights, we employ multiple perceptual weights generators.
3.4 Analysis of the Computational Cost
By not introducing additional visual tokens the input of the LLM, our VLoRA achieves higher computational efficiency for both training and inference. We only consider the computational cost of LLM, as the computational overhead of our perceptual weights generator is negligible in comparison. We assume the LLM has d blocks and hidden states dimension of h, the input text length is C, and the number of visual tokens is L. For convenience, we only consider the computational cost of the self-attention module and feed-forward network in LLM. The FLOPs of the self otf-aatention module and the feed-forward network are SLA² + 4 and 16Lh². For previous MILMs that align visual features the input space of LLM, the FLOPs of LLM are 24(L + d') + 4(L + 2h. For our VLoRA, the extra computational cost occurs in Eq. (6). where AWA A multiplied with AWB. Assuming that we generate perceptual weights for all types of weighs in ie decoder blocks. During training, we do not merge the perceptual weights with the LLM weights but use them as branches of the LLM weights. Therefore, the FLOPs are 24Cdh² + -C2 dh + 24krh² 12C + 14Ckh. For inference, the perceptual weights can be merged into the LLM, and the FLOPs are 24Cdh² 44C 24krh2 2 12kh2. Details of the FLOPs calculation are in the Appendix A. There is small increase in the overhead of training compared to inference, and we compare by the training FLOPs. Fig. 4, we compare the FLOPs of LLaVA and VLoRA. Our approach does not introduce additional computation the number of visual tokens increases, and our FLOPs are only 8% LLa 1.55's when the text length is 32.
Experiments
4.1 Implementation Details
Model Settings. We use Vicuna-7b-v1.55 [65] as our foundational LIM CLIP-VTT-L-14 [50] as vision encoder. The perceptual weights generator is initialized randomly. For the perceptual weights

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generator, we set the hidden size hp as 512, and the number of blocks N as 8. The rank of perceptual weights is 64. The number of perceptual queries is 8, which means that we insert perceptual weights AW only on 8 blocks, and in the implementation, for Vicuna-7b-vl 1.5 with 32 blocks, we insert A AW every 4 blocks. For better visual perceptual ability, we insert AW for all five types of weights L.M.. It is worth noting that the last & linear layers of the perceptual weights generator are zero-initialized as they are equivalent to the A B of LoRA weights, which are initialized as zero for training stability.
Pre-training Data. During pre-training, we use image-text pairs to train our model. Specifically, we use a subset of CapsFusion- 120M [59] with 30 million image-text pairs. CapsFusion- 120M randomly collects image-text pairs from LAION-COCO [1], which contains both web-crawled and synthetic captions generated by BLIP [28]. Then, a fine-tuned LLM used to integrate both types of captions.
Pre-training Configuration. We freeze the weights of L.LM and visual encoder in the pre-training stage, making only the perceptual weights generator trainable. We use the AdamW W [40] optimizer with a learning rate of 5e-5, which follows a linear warm-up and then a cosine decay schedule. The pre-training is conducted with total batch size of 768 for 40,000 iterations. The input images are resized to a resolution of 336 x 336. The pre-training stage uses 24 NVIDIA H800 GPUs for 7 hours.
Fine-tuning Data. For supervised fine-tuning, we adopt the same data LLaVA-v1.5. Specifi- cally, the supervised fine-tuning data is constructed with VQAv2 [13], GQA [19], OKVQA [45], OCRVQA [46], A-OKVQA [52], TextCaps [53], RefCOCO [44, 21]. Visual Genome [23], ShareGPT [2], and LLaVA-Insturee [36], with a total of 665K conversation data.
Fine-tuning Configuration. During the fine-tuning stage, we freeze the vision encoder and update the weights of the perceptual weights generator and LLM. The learning rate is set to 5e-5 and the learning rate schedule is the same as in the pre-training stage. The global batch size is 128. We train for one epoch on 8 NVIDIA H800 GPUs, which takes 2 hours.
4.2 Benchmarks for Evaluation
MMBench CCBench. MMBench [39] a comprehensive multimodal benchmark designed to evaluate the performance of MLLMs. It includes over 3,000 multiple-choice questions covering 20 ability categories. The evaluation is divided into perceptual and reasoning dimensions and subdivided into 20 categories. CCBench [39], released by the MMBench team, is designed for evaluating MLLMs the domain of Chinese Culture.
MME. MME [12] also measures the advanced MLLMs in terms of perception and cogmition, with a total of 14 subtasks. To minimize the influence of prompt engineering on MLLMs, the instructions of MME are designed as simple binary responses: "please answer yes or no".
Sci ScienceQA [42] is constructed from elementary and high school science curricula. Questions of ScienceQA span three subjects: natural science, language science, and social science. We use samples with images from the validation set to evaluate MLLMs.
HallusionBench. HallusionBench [14] is designed for evaluating image-context reasoning, including 346 images paired with 1 1129 questions crafted by human experts. Unlike other benchmarks [15, 30, 33] that focus on object hallucinations with limited topics and visual input types, HallusionBench considers both language hallucinations and visual illusions across diverse range of topics.
MMMU. MMMU [60] collects 11.5K multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines, spanning 30 subjects and 183 subfields, and comprising 30 heterogeneous image types. MMMU is more challenging than existing benchmarks due to the demand for college-level domain-specific knowledge.
4.3 Comparison with State-of-the-arts
Tab. compares our VLoRA with other state-of-the-art MLLMS on six MLLM benchmarks. The results are obtained from OpenCompass [8]. Unlike other MLLMs, our VLoRA does not require any visual tokens during LLM inference and has only 8% of the computational overhead of LLa when the text length is 32. On most benchmarks, VLoRA outperforms InstructBLIP, MiniGPT- 4, Idefics-instruct, and OpenFlamingo v2. Compared with Qwen- VL-Chat pre-trained on 1.4B image-text pairs, V has higher score 3.7 on MMBench and 1.3 on ScienceQA. Compared with VLa VLoRA can achieve comparable performance MMBench, ScienceQA, and
7

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Table 1: Comparisons on six MLLM benchmarks, including MMBench, MME, ScienceQA, Hallu- sionBench, MMMU, and CCBench. vis. tok. denotes the number of visual tokens involved in the LLM. Bolded numbers indicate the best results, and underlined numbers are the second-best results.
Model
Size
vis. tok.
MMBench
MME
ScienceQAA
Balch
MMMU
CCBench
InstructBL1P [9]
8B
32
36.0
1137.1
54.7
31.2
30.6
12.7
MiniGPT-4-vl [66]
12.2
770.6
39.0
31.9
23.6
MiniGPT-4vv2 [7]
256
24.3
708.4
54.1
30.0
25.0
Idefics-instruel [25]
9H
64
48.2
942
51.6
27.3
18.4
7.8
OpenFlamingo v2 [3, 4]
9B
6.6
535
45.7
29.4
28.2
6.3
Qwen-VL. [L.
256
38.2
334.1
Owen-VL-Chat
9.6B
256
60.6
1467.8
65.5
36.8
37.0
41.2
LLaVA-v1.5 [34]
7.2B
576
64.3
1510.7
66.8
27.6
35.7
27.5
1311.3
66.4
Table 2: Comparison LLaVA-v1.5 with various settings on six MLLM benchmarks, including MMBench, MME, ScienceQA, HallusionBench, MMMU, and CCBench. PT data represents the pre-training data. vis. tok. denotes the number of visual tokens involved in LLM.
Model
vis. tok.
MMBench
MME
SciencoA
HallusionBench
MMMU
CCBench
LLaVA-7b-v1.5 1..
blip-588k
576
64.3
1510.7
66.8
27.6
35.7
27.5
LLaVA-7b-v1.5
CapsFus-30m
576
1470.0
67.7
27.4
33.8
25,3
LLaVA-7b-v1.5-000 1.b.1..-QFormeee
CapsFus 30m
128
60.7
1241.5
67.3
26.7
33.8
25.3
VLoRA
CapsPus-30in
63.4
1311.3
66.4
26.4
33.7
28.6
HallusionBench and even better performance on CCBench. However, the results on MME fall short of V.L.VA-v1.5 since our perceptual weights generator is randomly initialized and necessitates more image-text pair data during the pre-training stage. To verify this, in Tab. 2, we reproduce I.La VA-11.5 by replacing the projector with a randomly initialized Q-Former and achieve similar results on MME. Our VLoRA achieves comparable performance to state-of-the-an art MLLMs without introducing visual tokens as LLM inputs, drastically reducing computational overhead.
Ablation Study
Currently, the performance of MLLMs is significantly affected by the foundational LLMs and the training data, including pre-training data and supervised fine-tuning data. To explore the effectiveness of our proposed paradigm and model, we perform a fair comparison with LLaVA-v1.5 [36] by adopting the same foundation LLM and training data in this section. Then, with this setting, we also explore the impact of different settings of each component on performance.
5.1 Comparison with LLaVA-v1.
To ensure fair comparison with LLaVA-v..5, we reproduce LLaVA-v1.5 with the same setting as our VLoRA, including the pre-training and supervised fine-tuning data. Furthermore, to eliminate the influence of the difference in the projector, we replace the project of V.Av1.- as randomly initialized Q-Former, which has the same number of blocks and hidden size as our perceptual weights generator. The training is conducted using the same pre- pre-training and fine-tuning data VLoRA.
In Tab. 2, the second row is the results of LLa VA-v1.5 pre-training on CapsFus-30mn With more pre-training data, VA-v1.. doesn't 't achieve significant improvement on MLLM benchmarks but rather drop on MME, HallusionBench, MMMU, and CCBench. Our VLoRA still comparable with the LLaVA-v1.5 training on the same data. The third row is the results of LLaVA-v1.5 with Q-Former, which is pre-trained on CapsFus-30m. We set the number of learnable queries as 128, thus the number of visual tokens is 128. Except for being slightly lower in ScienceQA and HallusionBennhh our VL.RA is significantly better on other MILLM benchmarks. These results demonstrate that our approach is comparable to even better than LLaVA-v1.5 with consistent settings.

---

Table 3: The impact of weights type that equipped perceptual weights. k, and denote the query, key, value, and output weights in the self-attention module, respectively. m denotes the weights of the feed-forward network.
Weights type
MMBench
MME
ScienceQA
HallusionBench
MMMU
CCBench
qkvom 63,4
1311.3
66.4
26.4
33.7
28.6
qkvm
59.6
1227.5
64.6
23.4
33.2
24.9
qkv
59.4
1267.9
65.8
23.2
33.9
28.8
qko 57.2
1240.5
64.0
23.4
34.6
24.9
qk 53.3
1169.8
65.0
23.5
32.0
21.8
Table 4: The impact of perceptual weights' rank. The rank of the generated perceptual weights indicates the extent visual information compression.
Rank
MMBench
MME
ScienceQA
HallusionBench
MMMU
CCBench
16
59.4
1212.7
67.1
22.9
33.7
24.5
32
60.7
1235.6
67.2
23.5
33.2
25.3
r 64
63.4
1311.3
66.4
26.4
33.7
28.6
128
61.0
1228.4
68.0
23.8
33.4
26.7
5.2
Analysis of each component
To further analyze VLoRA, we explore the impact of each component, including the type of weights that equipped perceptual weights, the rank of perceptual weights, and the number of blocks of perceptual weights generator.
The type of weights that equipped perceptual weights. As we mentioned in Sect. 3.1, there are five types of weights in the decoder block LLM,, which are query, key, value, output, and mlp. We explore the impact of inserting perceptual weights for different types of LLM weights. As shown Tab. 3, we compare different combinations, including gkvom, qkvm, qkv, qko, and qk. The model that equipped perceptual weights for all types of weights benchmarks. We notice that the performance of qkv is much better than qk. This suggests that the value matrix is essential for visual perception since the output of the value matrix will be weighted and summed, involving the results of the self-attention module.
achieve the best performance
most
The rank of perceptual weights. The rank of the generated perceptual weights represents the degree visual information compression. The smaller the rank, the more compressed the visual information. We compare the performance of rank from 16 to 128 in Tab. 4. When the 16, the visual information compressed severely in perceptual weights. However, LLM with such low-rank perceptual weights still perceive visual information. From on MMBench, MME, HallusionBench,, and CCBench improves with increasing rank. Specifically, the of MMBench increases from 57.6 to 63.4, and the score of MME increases from 1 166.88 to 1311.3. When the rank reaches 128, VLoRA's performance declines across these benchmarks. The reason might be that the visual information becomes redundant, and large rank may introduce noise into the perceptual weights, which hurts LLM's capability.
16 to 64, the performance
The number of blocks of perceptual weights generator. To explore the influence of the perceptual weights generator, we perform experiments with different numbers of blocks in the perceptual weights generator. In Tab. 5, observe that the performance of the weights generator with 8 blocks is better than with 4 blocks. However, when it comes to = 12, the scores on ScienceQA and CCBench are higher than with 8 blocks, but performance drops on other benchmarks. This suggests that while stronger perceptual weights generator can achieve better performance, there no benefit to increasing the number of blocks after the threshold is reached.
Conclusion
In this paper, instead of aligning visual features with the input space LLM,, we propose VLoRA to align visual features with the parameter space of LLM. By not introducing visual tokens into

---

Table 5: The impact of different numbers of blocks of perceptual weights generator.
Blocks
MMBench
MME
ScienceQA
HallusionBench
MMMU
CCBench
N 4
60.7
1289.3
63.9
24.4
32.0
26.7
N 8
63.4
1311.3
66.4
26.4
33.7
28.6
N 12
61.3
1289.3
67.1
25.5
33.8
30.2
LLM, our VLoRA can make LLM perceive visual information without extra computational overhead. To convert visual features into perceptual weights, propose the perceptual weights generator to generate low-rank perceptual weights for any weights LLM. Due to the low-rank property. the perceptual weights can be seen as LoRA weights, while AW, is generated and AWn WB is learnable. We perform comprehensive experiments on six MILMM benchmarks, and VLoRA can achieve comparable performance to V.L. in most benchmarks while only bringing 10% computational cost as LLaVA's. In the ablation study, we reproduce LLaVA-v1.5 under the same settings and show that our method can achieve better performance.
7 Limitations
Despite VLoRA's promising performance on various benchmarks, it still has some limitations. 1) Representing images as model weights a previously unexplored practice, and the extracted features from existing CLIP models may not be suitable to be converted into model weights. It is necessary to explore vision encoder that is more suitable for this paradigm. 2) We use one perceptual weights generator for one type of weight, which may lead to an insufficient correlation between different types generated perceptual weights. may be better use the same perceptual weights generator to produce weights for all types at once.
Acknowledgments and Disclosure of Funding
This work was in part supported by the National Natural Science Foundation of China under grants 62032006 and 62021001, and by the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center under grants KTO03 and 23YGXT001. Mike Shou does not receive any funding for this work.
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A Analysis of VLoRA computational overhead
In this subsection, we give a detailed calculation of the computational overhead of VLoRA. Similar to Sect. 3,4, we assume the LLM has d blocks and hidden states dimension of h, the input text length C, and the number of visual tokens L. Therefore, the FLOPs of the self-attention module and the feed-forward network SLh² + +4L and 16Lhº. Since visual tokens not introduced, then LLM has a computational overhead of 24Cdh² + d4 for text token sequence input. For training, we use perceptual weights branches of L.M weights. The extra computation comes from three parts: 1) the matrix multiplication of the two perceptual weights with FLOPs of 24krh2 2) The multiplication of the text token and the perceptual weights with FL.OPs of 12Ckh2 3) The output coming of the perceptual weights is to be added to the output of the L.M weights with FLOPs 14Ckh. Therefore, the total FLOPs VLoRA during training is 24Cdh² 4C + 24kr + 12Ck + 14Ckh. For inference, we merge the perceptual weights with LLM's weights. The extra computation comes from two parts: the matrix multiplication of the two perceptual weights with FLOPs of 24krh², which is the same as training. 2) Adding perceptual weights to LLM weights with FLOPs of 12kh?. The total FLOPs during inference are 24Cdh² 4C 24krh? + 12kh?.
B Visualization Results
VLoRA can achieve promising results on various MILMM benchmarks, but these benchmarks are either multiple choice judgmental, and to demonstrate VLoRA's capabilities even further, show some real-world samples Fig. 5. The first figure suggests that our VLoRA can count the accurate number of steaks in the image. The second figure shows that VLoRA has sufficient common sense. In the third figure, VLoRA demonstrates the ability to reason and have long text conversations.
Whi is the nver golden
The river golden yollow due reffection
sunlight
suriigtt
vibrent glow the surfeceod the river,
surfece. Ihis phenomenon mon
common
water.
sunset, when the sunlighe
O How many steats areir the image? A There two steaks in the image?
Who is the director of this 1im?
beautffll
limes Cameron
colortu
Figure 5: Visualization results of VLoRA. This figure demonstrates the capabilities of our VLoRA in real-world scenarios, including accurate counting and common sense reasoning.
C Broader Impacts
Our proposed new paradigm significantly improves the training and inference efficiency of multimodal reduce the
large models and reduces the computational overhead, which, in terms of research, resource threshold of multimodal large model research, which is conducive to the active exploration of researchers in related fields, and, in terms of practical application, reduces the cost of large-scale deployment for use and helps to reduce the consumption of resources.
D Comparisons on Fine-grained Benchmarks
We provide more results on fine-grained benchmarks for comprehensive comparisons, including TextVQA, VQA, InfoVQA, and OCRBench. As shown in Tab. 6. these fine-grained bench- marks. VLoRA's performance has gap compared to LLaVA on TextVQA and DocVQA, but it can achieve comparable results on Info VLoRA converts CLIP's visual features into model weights, but CLIP's 's visual features are aligned with text rather than model parameters. Therefore, we need more diverse data allow the weights generator to learn this transformation well. Since our pre-training data is coarse graine- image captioning data and the amount of fine-tuning data limited, the performance of VLoRA trained on this dataset not as good L.La VA in some fine-gra fine-grained tasks.
14

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Table 6: Comparisons between VLoRA and LLaVA-v1.5 on fine-grained benchmarks, including TextVQA, DocVQA, InfoVQA, and OCRBench.
Model
Size
# vis. tok.
TextVQA
DocVQA
InfoVQA
OCRBench
Avg.
LLa [34]
7.2B
576
58.2
18.4
20.4
31.8
28.0
VLoRA 7.8B
51.4
13.4
19.5
27.7
25.8
Table 7: Comparisons of Training Speed and GPU Memory Requirements between VLoRA and LLaVA-v1.5
pre-training LLaVA
pre-training VLoRA
fine-tuning LLaVA
fine-tuning VLoRA
Training Speed
106 samples/s
246 samples/s
samples/s 79G
samples/s
GPU RAM
79G
58.6G
79G
E
Analysis of Training and Inference Efficiency
E.1
Training Efficiency Analysis
As shown in Tab. 7. in the pre-training stage, the training speed of VLoRA can be 2.3 times faster than LLaVA. LLaVA's peak memory usage is 79G, while VLoRA's is significantly less at 58.6G. In the tinettuning phase, VLoRA maintains considerable advantage in training speed and can train 73 samples per second, 1.6 times faster than LLaVA. The memory usage of both is similar, around 79G, due to the learnable parameters of the LLM being the primary contributors memory usage.
E.2 Inference Efficiency Analysis
During the prefilling stage, VLoRA saves time by not calculating the kv cache for visual tokens. In the decoding stage, VLoRA decreases the time needed to calculate attention scores with visual tokens for each new token. As a result, VLoRA maintains an advantage in inference efficiency, even when generating long sentences.
Prefilling stage. Using a single A 100 with flash attention, LLaVA VA takes 65 ms to produce first token, whereas VLoRA only takes 45 ms. The primary time consumption for VLoRA is in weight generation, which has optimization potential, such as employing a single weight generator for all weight types.
Decoding stage. With generated sequence length set at 256, and using Flash Attention, KV Cache, and Batch Inference to maximize speed on single A A100, the inference speed of LLaVA is 410 tokens per second. In contrast, VLoRA achieves 1078 tokens per second, which 2.6 times faster than LLaVA.
NeurIPS Paper Checklist
1. Claims
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Reviews:
[{"review_id": "jMDMI1GTJq", "reviewer": "Reviewer_cXpd", "summary": "The work proposes an efficient setup for incorporating non-text modalities into pretrained LLMs for reasoning-based tasks. Instead of introducing new tokens into the LLM, they propose to dynamically generate LoRA weight matrix residuals for the linear projectors within the LLM, conditioned on the input image. The weight matrix updates, when applied to the LLM, will then alter how the LLM processes the input text.\n\nExperimental results demonstrate competitive performance on QA tasks without the quadratic cost associated with increased input tokens.", "questions": "See weaknesses", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 2, "strengths": "- Simple and efficient approach with effective results\n- Detailed analysis on architecture and the effect of the rank of the update matrix.", "weaknesses": "- I think it's worthwhile to look beyond the QA benchmark numbers to understand what the implications of this architectural change are. If I am to understand this method correctly, the weight generator is not conditioned on the text-to-be-ingested by the LLM, which means it could drop information not typically useful for the task it's trained on. It would be curious to see how this approach compares to the more standard approach when it comes to asking questions about very obscure (or spatially tin) elements within an image.\n- The approach also appears to have a pretty sensitive sweet spot for rank, which could be expensive to tune for\n- From the perspective of technical novelty, I believe this is closely related to HyperNetworks https://arxiv.org/pdf/1609.09106 of which there are also transformer variants: https://arxiv.org/pdf/2106.04489 . The authors should probably include a relevant related-works section for this as well, and perhaps some additional comparisons against adaptation techniques proposed there as well. I think the goal would be to demonstrate that the proposed approach works best when it comes to sample-level adaptations, as compared to the typical task-level adaptations.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the insightful comments provided by the reviewer.\n\n**Question 1: Look beyond QA benchmark numbers to understand what the implications of this architectural change are.**\n\nThank you for your suggestion. We have provided some practical examples in the PDF file, where you can see the impact of architectural changes. We conducted tests on practical examples. From the samples on the left, we can see that both VLoRA and LLaVA can recognize the fine-grained car logos in the image. However, for the text recognition on the right, both models ignored the target area in the instruction and made mistakes in their answers.\n\n**Question 2: Whether weights generator could drop information not typically useful for the task it's trained on.**\n\nOur weights generator of VLoRA is conditioned solely on the input image, which requires the generated LoRA weights to contain as much comprehensive image information as possible, rather than pre-extracting the information needed for LLM based on the input text. This design is beneficial for the model's generalization ability.\n\nHowever, weights generator also requires a large amount of diverse pre-training data to train the model. In situations where the diversity and quantity of data are insufficient, the weights generator may lose necessary information. Our model is pre-trained only on the image captioning dataset, where diversity is not guaranteed, so it is possible to lose some information. To measure this potential loss, we evaluated our model on some zero-shot fine-grained or unconventional benchmarks. We evaluated our model on Text-central benchmarks like AI2D, InfoVQA and Seed2 Plus, and mathematical benchmark MathVision (math-related tasks are rare in training data), BLINK (tasks like multi-perspective reasoning, depth estimation, and reflexive estimation, which are also rare in training data). \n\n| method | AI2D  | InfoVQA | Seed2 Plus | MathVision | Blink | Average |\n| ------ | ----- | ------- | ---------- | ---------- | ----- | ------- |\n| LLaVA  | 55.5  | 20.4    | 40.05      | 8.52       | 39.7  | 32.8    |\n| VLoRA  | 54.01 | 19.46   | 37.5       | 11.7       | 39.8  | 32.5    |\n\nThe results show that VLoRA's performance on unconventional tasks is comparable, but there is still a gap in text-related tasks. The reason is our pre-training data is limited and only consisting of coarse-grained image captioning data.\n\n**Question 3: It would be curious to see how this approach compares to the more standard approach when it comes to asking questions about very obscure (or spatially tin) elements within an image.**\n\nWe provided serveal examples in Q1, and in Q2, we perform results on zero-shot fine-grained or unconventional benchmarks.\n\n**Question 4: Whether this approach is sensitive to the setting of rank, which could be expensive to tune for.**\n\nOur approach is not sensitive to the setting of rank.  In Tab 4, for a fair comparison, we use the same dataset Capsfusion-30M for pre-training. This dataset is insufficient for the scenario where the rank is set to 128, leading to a performance degradation in this case. A higher rank signifies that the perceptual weights generated by the weights generator have a larger dimension, which poses a greater demand on the weights generator. Consequently, a larger amount of pre-training data is required to effectively train the weights generator. In the below table,  we increase the pre-training data to 60M, it can be observed that the performance of a rank of 128 becomes comparable to that of a rank of 64.\n\n| rank | data           | MMBench | MME          | ScienceQA | HallusionBench | MMMU | CCBench | Average |\n| ---- | -------------- | ------- | ------------ | --------- | -------------- | ---- | ------- | ------- |\n| 64   | Capsfusion-30M | 63.4    | 1311.3(65.6) | 66.4      | 26.4           | 36.0 | 28.6    | 47.7    |\n| 128  | Capsfusion-30M | 61.0    | 1228.4(61.4) | 68.0      | 23.8           | 33.4 | 26.7    | 45.7    |\n| 128  | Capsfusion-60M | 62.8    | 1337(66.9)   | 65.5      | 25.8           | 33.4 | 30.2    | 47.4    |\n\n**Question 5: Discussion with more related work.**\n\nWe very appreciate the related work reviewer have pointed out. We will incorporate these discussions into our paper. Below are our discussions.\n\nHyperNetworks proposes static hypternetwork for CNN and dynamic hypternetwork for RNN. HyperFormer proposes hypterformer to generate adapter parameters for all layers and multiple tasks using shared hypternetworks.  The parameter generation of both methods is designed on task-level for pre-defined tasks. \n\nDifferent from them,  1) VLoRA focuses on sample-level parameter generation, the generated LoRA weights are conditioned on the input image without pre-defining tasks during training. Because the target of MLLM is to address a variety of  tasks or problems, which are difficult to fully define in advance. Therefore, task-level adaption is unsuitable for recent MLLM. 2) VLoRA utilizes the generated parameters in LoRA way. Sample-level parameter generation can lead to significant changes in model parameters. VLoRA, adopting the LoRA method, can better maintain the inherent capability of the pre-trained LLM.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3RTcfApOVl", "reviewer": "Reviewer_MX94", "summary": "The paper proposes a novel way to enable visual understanding in LLMs. Instead of encoding image as visual tokens, the paper proposes converting visual input to low-rank perceptual weights which are merged with LLM weights (similar to LoRA). The paper shows that the proposed approach achieves comparable performance on various single image V+L benchmarks, while significantly reducing computational cost.", "questions": "- Can the authors comment on memory overhead during training? I imagine that storing the weights of perceptual weights generator (especially using different ones for different weight type) is expensive? \n- What is the red dotted vertical line in Figure 4 (Left)", "limitations": "The authors discuss some limitations, but miss many as pointed out in the weakness section. E.g., theoretical vs practical benefit when utilising modern tricks to speed up inference, as well as how to model multiple images.", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper presents a novel idea of encoding visual information as low-rank perceptual weights instead of visual tokens. It's a fresh perspective on how to integrate visual knowledge in LLMs which hasn't been done before. \n- The results show that the performance is on-par with existing methods on most standard V+L benchmarks, while requiring significantly less computational overhead (as measured by GFLOPS). \n- The authors show exhaustive ablations for the perceptual weights generator which were quite insightful.", "weaknesses": "While the method is interesting, and novel,  several practical questions remain that affect the flexibility of that method:  \n- How will the model work when more than one image is used as input (such as interleaved image-text dialogue, videos, etc).   \n- While the method achieves better GFLOPS than existing paradigm of using visual tokens, practical advancements (FlashAttention, KVCaching) significantly reduce actual computational overhead of adding more tokens. Can the authors comment on how their model compares after accounting for these tricks that people use to speed up inference? The authors can consider reporting tokens/s, and time to first token instead of GFLOPs. \n- I would have liked to see results on benchmarks which require fine-grained image understanding and spatial understanding and will potentially benefit from high-resolution like TextVQA, OKVQA, DocVQA.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We are grateful for the effort the reviewer has dedicated to evaluating this work.\n\n**Question 1: How will the model work when more than one image is used as input (such as interleaved image-text dialogue, videos, etc).**\n\nVLoRA can naturally be extended to support multiple image inputs, here we consider three scenarios for multi-image input.\n\n**1) Multimodal In-Context Learning.** In this scenario, the input to MLLM will provide multiple image-text pairs as examples to assist in answering the query image and question. We can use the weights generator to create multimodal LoRA weights for each in-context example, using both image and text as input. For the query image and question input, we generate query LoRA weights  from the query image, and the question is input to the LLM.\n\n**2) Interleaved Image-Text Dialogue.** In this case, images and text have a temporal relationship. Given input $C = \\{V_1, T_1,..., V_N, T_N\\}$, we generate $N$ LoRA weight sets, $W = \\{W_1,..., W_N\\}$, corresponding to $N$ images. Text $T=\\{T_1, T_2, ..., T_N\\}$ is input to the LLM. During training and inference, tokens of $T_j$ pass through the matrix of $W_i$ where $i ≤ j$. For instance, $T_1$ tokens pass through $W_1$ and $T_2$ tokens pass through $W_1$ and $W_2$. This ensures text tokens only attend to preceding image information, maintaining causality.\n\n**3) Video Input.** We can represent different video frames with multiple sets of LoRA weights, and in order to maintain the temporal relationship between video frames, we can add learnable position encodings to the different LoRA weights. We can also extract the representation of the entire video, and then generate a single LoRA weight from the video representation to represent the video information.\n\nVLoRA offers a new MLLM paradigm. This paper focuses more on validating its feasibility in general scenarios, and the extension to multi-image input can be considered as future work for further exploration.\n\n**Question 2: Compare VLoRA's efficiency considering practical advancements like FlashAttention and KVCaching, and report tokens per second and time to first token.**\n\nWhen using the same acceleration techniques, compared to LLaVA, VLoRA still has significant efficiency advantages during both training and inference. We discuss the efficiency of VLoRA during the training and inference, and compare with LLaVA under the same machine. \n\n**Training efficiency.** The training has pre-training and fine-tuning stages, and Flash Attention technique was used for training.\n\n|                | pre-training LLaVA | pre-training VLoRA | fine-tuning LLaVA | fine-tuning VLoRA |\n| - | - | - | - | - |\n| training speed | 106 samples/s     | 246 samples/s | 46 samples/s | 73 samples/s |\n\nIn the pre-training stage, VLoRA can be 2.3 times faster than LLaVA. In the fine-tuning stage, VLoRA still maintains a considerable advantage and can train 73 samples per second, which is 1.6 times faster than LLaVA.\n\n**Inference efficiency.** In the prefilling stage, VLoRA reduces the time of calculating the kv cache of visual tokens. In the decoding stage, VLoRA reduces the time to compute attention scores with visual tokens for each new token.\n\n1) Prefilling stage. Using a single A100 with flash attention, LLaVA's time to first token is **65 ms**, while VLoRA's is **45 ms**. VLoRA's primary time consumption is in weight generation, which has optimization potential, such as using a single weights generator for all weights type. \n\n2. Decoding stage. We set the generated sequence length at 256 and employ Flash Attention, KV Cache, and Batch Inference to achieve the maximum inference speed. The inference is performed on a single A100. The inference speed of LLaVA is **410 tokens/s**, while that of VLoRA is **1078 tokens/s**, which is **2.6 times** that of LLaVA. \n\n**Question 3: More results on fine-grained benchmarks.**\n\nWe provide more results on fine-grained benchmarks, including TextVQA, DocVQA, and other fine-grained benchmarks, like OCRBench and InfoVQA. Due to time constraints, we can't complete the evaluation of OK-VQA  in time to provide results.\n\n| method | OCRBench | InfoVQA | TextVQA | DocVQA | Average |\n| - | - | - | - | - | - |\n| LLaVA  | 31.8     | 20.4    | 58.2    | 18.4   | 28.0    |\n| VLoRA  | 27.7     | 19.5    | 51.4    | 13.4   | 25.8    |\n\nOn these fine-grained benchmarks, VLoRA's performance has a gap compared to LLaVA on TextVQA and DocVQA, but it can achieve comparable results on InfoVQA. VLoRA converts CLIP's visual features into model weights, but CLIP's visual features are aligned with text rather than model parameters. Therefore, we need more diverse data to allow the weights generator to learn this transformation well. Since our pre-training data is coarse-grained image captioning data and amount of fine-tuning data is limited, the performance of VLoRA trained on this dataset is not as good as LLaVA in some fine-grained tasks. \n\nThe purpose of VLoRA is to provide a new parameter-space-aligned MLLM paradigm, and we focus more on the general scenarios. Compared to the well-developed LLaVA, there are still many areas to explore in this new paradigm, including training data and visual encoders, which are also part of our future work.\n\n**Question 4: Memory overhead during training and inference.**\n\nVLoRA's memory usage is lower compared to LLaVA. LLaVA's 576 visual tokens per layer result in higher memory overhead than VLoRA's weight generators.  In pre-training, LLaVA uses 79G, and VLoRA uses 58.6G memory. During fine-tuning, both use approximately 79G due to LLM's learnable parameters. In inference, with a batch size of 16 and sequence length of 512, LLaVA uses 39G, and VLoRA uses 35G memory.\n\n**Question 5: The meaning of the red dotted vertical line in Figure 4 (left)**\n\nThe red dotted vertical line in Figure 4 (left) represents the position where the number of visual tokens is 576, which is the number of input visual tokens for LLaVA.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "etLxR3tV9J", "reviewer": "Reviewer_s5rD", "summary": "This paper presents VloRA, a paradigm for building MLLMs, which aligns visual features with the parameter space of LLMs. By representing visual information as model weights, no visual tokens is need in the input, which reduces the length of the input sequence and improves efficiency.", "questions": "(1) Please provide the experimental data about the real training speed and GPU RAM requirement of VLoRA and LLaVA.\n(2) Please provide the experimental data on the inference efficiency of VLoRA and LLaVA, especially on long-sequence generation.\n(3) Some recently similar work should be discussed in detail.\n(4) How about the results on VQAv2, GQA, TextVQA, VisWiz, POPE, SEED, MM-Vet.\n(5) Why use CapsFus-30m instead of blip-558k for pretraining? Can VeLoRA still be competitive when using a smaller blip-558k for pretraining?", "limitations": "This paper discusses some of the method's limitations, but I believe the discussion can be more comprehensive. See the 'Weaknesses' mentioned above.", "rating": 5, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "(1) The motivation is convincing and the problem to solve is important. The enormous computational cost limits the training and inference devices for MLLMs. \n(2) VLoRA significantly reduces the FLOPs of MLLMs both in training and inference. Moreover, although not mentioned in the paper, I believe VLoRA can also reduce the consumption of GPU RAM.\n(3) Experimental results show that VLoRA maintains competitive performance.", "weaknesses": "(1) The paper presents the FLOPs advantage of VLoRA. However, FLOPs cannot reflect the real latency of LLMs sometimes, especially when generating a long sentence. The generation of LLMs has two stages: prefilling (calculating the KV cache and generating the first token) and decoding (generating subsequent tokens one-by-one). When generating a long sentence such as image captioning, shorter inputs can significantly reduce the prefilling time, but the decoding time is primarily determined by the length of the output. \n(2)The experiments are not sufficiently comprehensive. To benchmark against LLaVA-1.5, the (zero-shot) comparisons should also be conducted on the following tasks: VQAv2, GQA, TextVQA, VisWiz, POPE, SEED, MM-Vet. I am particularly curious about the results of TextVQA because previous papers have shown that the performance on this dataset is strongly correlated with the number of visual tokens.\n(3) VLoRA can be regarded as using vision features to generate the PEFT parameters (LoRA) of LLMs. Therefore, some similar work listed as following should be discussed in detail:\n\n[1] HyperPELT: Unified Parameter-Efficient Language Model Tuning for Both Language and Vision-and-Language Tasks. ACL 2023 Findings.\n[2] LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention. ICLR 2024.\n[3] Memory-Space Visual Prompting for Efficient Vision-Language Fine-Tuning. ICML 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We are deeply grateful for the reviewer's valuable comments.\n\n**Question 1: The experimental data about the real training speed and GPU RAM requirement of VLoRA and LLaVA**\n\n|                | pre-training LLaVA | pre-training VLoRA | fine-tuning LLaVA | fine-tuning VLoRA |\n| - | - | - | - | - |\n| training speed | 106 samples/s | 246 samples/s | 46 samples/s | 73 samples/s |\n| GPU RAM        | 79G              | 58.6G            | 79G             | 79G             |\n\n1) In the pre-training stage, the training speed of VLoRA can be 2.3 times faster than LLaVA. And LLaVA's peak memory usage is 79G, while VLoRA's is significantly less at 58.6G. \n\n2. In the fine-tuning phase, VLoRA still maintains a considerable advantage in training speed and can train 73 samples per second, which is 1.6 times faster than LLaVA. The memory usage of both is similar, around 79G, due to the learnable parameters of the LLM being the primary contributors to memory usage.\n\n**Question 2:  The experimental data on the inference efficiency of VLoRA and LLaVA, including long-sequence generation.**\n\nDuring the prefilling stage, VLoRA saves the time of calculating the kv cache of visual tokens. In the decoding stage, VLoRA decreases the time needed to calculate attention scores with visual tokens for each new token. Therefore, even when generating a long sentence, VLoRA's inference efficiency still has an advantage.\n\nWe compare the inference speed of VLoRA and LLaVA on a single A100, and utilize KV Cache, FlashAttention, and Batch Inference techniques to achieve the maximum practical inference speed. \n\n| num of generated tokens | VLoRA (tokens/s) | LLaVA (tokens/s) | Speed Ratio |\n| - | - | - | - |\n| 256                     | 1078             | 410              | 2.6         |\n| 512                     | 865              | 342              | 2.5         |\n| 1024                    | 451              | 250              | 1.8         |\n\nWith a generated sequence length of 256, VLoRA's generation speed is 1078 tokens/s, 2.6 times faster than LLaVA. At a length of 512, VLoRA remains 2.5 times faster. We find that VLoRA still maintains an advantage as sequence length increases. Even at a length of 1024, VLoRA's speed is 1.8 times faster than that of LLaVA.\n\n**Question 3: Discussion with recent similar work.**\n\nWe greatly appreciate the relevant work that the reviewer has highlighted. We will integrate these discussions into our paper. The following are our discussions. \n\nLLaMA-Adapter inserts learnable prompts into L of N decoder layers and uses zero-initialized attention for stable training. HyperPELT employs a shared hypernetwork that generates weights for fine-tuning various modules. MemVP concatenates visual prompts with FFN weights for injecting visual knowledge. In contrast, 1) VLoRA can inject visual information at any linear module, offering flexibility. 2) Unlike task-level PEFT methods, VLoRA is sample-level, generating weights for individual input images. Our evaluations, mainly in zero-shot settings, demonstrate VLoRA's strong generalization ability. \n\n**Question 4: The zero-shot comparisons results on VQAv2, GQA, TextVQA, VisWiz, POPE, SEED, MM-Vet.**\n\nThese datasets are more fine-grained, but among them, VQAv2, GQA, and TextVQA are not zero-shot. To make a  zero-shot comparison, we also evaluated on other zero-shot fine-grained datasets, including OCRBench, AI2D, InfoVQA, MathVision (math-related), SeedBench-2 (SeedBench series), SeedBench-2 Plus (Text-related), and BLINK (difficult visual perception tasks).\n\n| Method | textvqa | docvqa | vqav2 | gqa  | viswiz | pope | seed | mmvet | ocrb | ai2d | infovqa | mathvision | seed2 | seed2 plus | blink | Avg. |\n| - | - |  - | - | - | - | - | - | - | - | - | - | - | - | - | - |  - |\n| LLaVA  | 58.2 | 18.39 | 78.5 | 61 | 50 | 86.1 | 65.8 | 32.9 | 31.8 | 55.5 | 20.4 | 8.52 | 43.4 | 40.05 | 39.7 |46.1|\n| VLoRA  | 51.43 | 13.41 | 71.5 | 51.42 | 41.31 | 80.5 | 54.6 | 25.8 | 27.7 | 54.01 | 19.46 | 11.7 | 44.92 | 37.5 | 39.8 | 41.7 |\n\nWe find that VLoRA's performance on TextVQA, DocVQA, and VQAv2 has a gap compared to LLaVA, but on other fine-grained benchmarks such as AI2D and InfoVQA, its performance is comparable to LLaVA's.\n\nTherefore, although VLoRA has high training and inference efficiency and achieves considerable performance on general benchmarks, there is still room for improvement in this method on fine-grained benchmarks.\n\nThe possible reasons are: 1) The lack of diverse training data. VLoRA transforms CLIP's visual features into model weights, but CLIP's visual features are aligned with text rather than model weights. Therefore, diverse data is needed to allow the weights generator to retain sufficient visual information when transforming visual features into model weights. However, VLoRA is pre-trained on CapsFus-30M, a coarse-grained image captioning data, which limited the performance of VLoRA.  2) The data ratio has not been adjusted. The ratio of different types of data is crucial to the performance of MLLMs. Our model architecture is completely different from methods like LLaVA that align visual tokens to the input space, so the data ratio should be readjusted.\n\nThe purpose of VLoRA is to provide an efficient new parameter-space-aligned MLLM paradigm, and in this paper, we focus on general scenarios. Compared to the well-developed LLaVA, there are still many areas to explore in this new paradigm, including training data and visual encoders, which are also part of our future work.\n\n**Question 5: The reason of using CapsFus-30M instead of blip-558k for pretraining.**\n\nThe reason is that our weights generator has more learnable parameters than LLaVA's projector, and needs to learn to transform CLIP's visual features into model weights. Therefore, we need to use CapsFus-30M instead of blip-558k. If only pre-trained with blip-558k, the generator struggles to convert visual features into model weights, resulting in reduced VLoRA performance.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
The figure requested by reviewer cXpd has been included in the PDF

Meta-review: The work presents a novel approach for integrating visual inputs in an LLM by predicting and fusing weights instead of features. The work initially received 3BA. Post-rebuttal one of the reviewers raised their rating to WA. In the reviewer discussion phase, the reviewers showed their support for acceptance in spite of some limitations (not all results being SOTA and other practical considerations). The reviewers especially appreciated the "fresh perspective with substantial experiments and analysis", "good insights",  and the work "testing out a rather simple idea, as opposed to many recent papers which present larger, complex, and often ad-hoc solutions with very little insight". 

Having read the reviews, rebuttal and the following discussions, the AC agrees with the reviewers and recommends "accept". Congratulations to the authors!
Decision: Accept (poster)