Dense Connector for MLLMs

Authors: huanjin  yao
wenhao  wu
taojiannan  yang
yuxin  song
mengxi  zhang
haocheng  feng
yifan  sun
zhiheng  li
wanli  ouyang
jingdong  wang
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-09 06:24:36

Abstract: *Do we fully leverage the potential of visual encoder in Multimodal Large Language Models (MLLMs)?* The recent outstanding performance of MLLMs in multimodal understanding has garnered broad attention from both academia and industry. In the current MLLM rat race, the focus seems to be predominantly on the linguistic side. We witness the rise of larger and higher-quality instruction datasets, as well as the involvement of larger-sized LLMs. Yet, scant attention has been directed towards the visual signals utilized by MLLMs, often assumed to be the final high-level features extracted by a frozen visual encoder. In this paper, we introduce the **Dense Connector** - a simple, effective, and plug-and-play vision-language connector that significantly enhances existing MLLMs by leveraging multi-layer visual features, with minimal additional computational overhead. Building on this, we also propose the Efficient Dense Connector, which achieves performance comparable to LLaVA-v1.5 with only 25% of the visual tokens. Furthermore, our model, trained solely on images, showcases remarkable zero-shot capabilities in video understanding as well. Experimental results across various vision encoders, image resolutions, training dataset scales, varying sizes of LLMs (2.7B→70B), and diverse architectures of MLLMs (e.g., LLaVA-v1.5, LLaVA-NeXT and Mini-Gemini) validate the versatility and scalability of our approach, achieving state-of-the-art performance across 19 image and video benchmarks. We hope that this work will provide valuable experience and serve as a basic module for future MLLM development. Code is available at https://github.com/HJYao00/DenseConnector.

Main paper: 
 introduction: 
 In recent years, Large Language Models (LLMs) led by ChatGPT [1] have made remarkable advancements in text comprehension and generation. Furthermore, cutting-edge Multimodal Large Language Models (MLLMs) [2, 3] have rapidly expanded the capabilities of LLMs to include visual understanding, evolving into models capable of integrating both vision and text modalities. This has elevated MLLMs to become a new focal point for research and discussion [4, 5, 6, 7] .In broad terms, the architecture of existing MLLMs can be delineated into three components: the pre-trained vision encoder (e.g., CLIP's ViT-L [8] or EVA-CLIP's ViT-G [9] ), the pre-trained LLM (e.g., OPT [10] , Llama [11] , Vicuna [12] , etc.), and the connector (e.g., Q-former [13, 14] or linear projection [15, 16] ) trained from scratch to bridge the vision and language models. An intriguing trend in current MLLM research is that the focus of model learning and performance improvement seems to primarily center around the language aspect (e.g., utilizing larger-scale and higher-quality visual instruction data [17, 16, 18] , larger-sized LLMs [19, 20] ), with less exploration into the visual signals fed into the connector. Typically, the visual encoder is frozen to extract high-level visual features, which are then fed into the connector. This leads us to rethink: Have we fully utilized the existing pre-trained visual encoder? In addition to the common practice of feeding the connector with final high-level visual features from visual encoder, an intuitive yet overlooked idea is to integrate visual features from various layers to complement the high-level features. In Fig. 1 (a), we illustrate attention maps from different layers of a 24-layer CLIP [8] pre-trained ViT-L [21] , showing that different layers of the same visual encoder emphasize different regions of interest. Moreover, looking back at the history of computer vision, classic models (e.g., Densenet [22] , FPN [23] ) utilize multi-layer features to enhance visual representations. In MLLMs, the vision encoder is typically frozen to mitigate significant computational costs. In this context, our idea leverages the "free lunch" of utilizing offline features from different layers as an implicit enhancement of visual information without the need for additional computational overhead. Furthermore, this way also complements techniques that directly increase visual signals, e.g., increasing image resolution [18, 24, 25, 26, 27, 28] or introducing additional visual encoders [29, 30, 18] . This idea is both simple and efficient, while also being sufficiently generic, logically allowing for seamless integration with any existing MLLMs.In light of this, we propose the Dense Connector (DC), serving as a plug-and-play vision-language connector that involves offline features from various layers of the frozen visual encoder to provide the LLM with more visual cues. We explore three intuitive instantiations for the Dense Connector: 1) Sparse Token Integration (STI): We explicitly consider increasing the number of visual tokens by aggregating visual tokens from different specified layers and the final visual token. These tokens are then fed into a learnable projector for mapping into the text space. 2) Sparse Channel Integration (SCI): To avoid increasing the number of tokens, we concatenate visual tokens from different specified layers in the feature dimension. They are then passed to the projector, which not only maps visual tokens into the text space but also serves to reduce the feature dimensionality. 3) Dense Channel Integration (DCI): In addition to incorporating features from specified layers, we further attempt to utilize visual features from all layers. All of these instantiations yield significant improvements while utilizing just one simple learnable projector (comprising two linear layers) without introducing any extra parameters. Moreover, we conduct extensive empirical studies to demonstrate its scalability and compatibility. We summarize our contributions as follows:• We propose a simple, effective, and plug-and-play Dense Connector that enhances the visual representation of existing MLLMs with minimal additional computational overhead. We hope it can serve as a basic module to continuously benefit future MLLMs.• We demonstrate the versatility and scalability of our approach across various visual encoders, image resolutions (336px→768px), training dataset scales, varying sizes of LLMs (2B→70B), and diverse MLLMs architectures (e.g., LLaVA-v1.5 [16] , LLaVA-NeXT [25] , Mini-Gemini [18] ).• Our method exhibits exceptional performance across 11 image benchmarks and achieves state-of-the-art results on 8 video benchmarks without the need for specific video tuning.2 Related Work 
large pre-trained vision models: 
 The advent of pre-trained Vision Transformers (ViT) [21] has significantly propelled the advancement of computer vision. Furthermore, pre-training ViT models on web-scale image-text pairs, e.g., CLIP [8] and its subsequent iterations [9, 31, 32, 33] , where vision and text encoders are simultaneously trained to bridge the gap between visual and textual modalities, has introduced zero-shot visual perception capabilities. Since then, CLIP-like models have served as effective initializations and have been incorporated into various vision-language cross-modal models (e.g., video-text alignment [34, 35, 36, 37] , large vision-language models [14, 15, 38] , etc.). Recently, SigLIP [31] introduced pairwise sigmoid loss during training, enabling the visual encoder to demonstrate more advanced visual perception capabilities. To validate the compatibility of our Dense Connector, this paper conducted experiments on different visual encoders, including those of CLIP [8] and SigLIP [31] .The exceptional text understanding and generation capabilities demonstrated by auto-regressive Large Language Models (LLMs) [39, 40, 41] have garnered significant attention. Subsequently, a plethora of LLMs [42, 11, 10, 43] have emerged, with notable open-source efforts like LLaMA [42] greatly propelling community contributions to LLMs research. Through instruction fine-tuning techniques, these models showcase human-like language interaction abilities, further further propelling advancements in natural language processing. Recent developments have seen LLMs scaled up or down to meet various application needs. Lightweight LLMs [44, 45, 20, 46] have been developed to address computational constraints, facilitating edge deployment. Conversely, in the pursuit of exploring the upper limits of LLMs, works such as [47, 19, 11, 20] have expanded LLM parameters, continuously pushing the boundaries of language capabilities. In this study, we validated the scalability of our Dense Connector by employing multiple LLMs ranging from 2.7B to 70B parameters.After witnessing the success of LLMs, researchers have shifted their focus towards enabling LLMs to understand visual signals. To achieve this, prior research has proposed compressing visual embeddings using Q-former [14] into query embeddings, followed by transforming them into text embeddings through linear projection, or directly employing MLP projection [15] to connect the visual encoder with LLM. Furthermore, following the instruction tuning paradigm [48, 49] , pioneering works [38, 15, 50] significantly boost the development of MLLMs through visual instruction tuning. Subsequently, by introducing larger-scale and higher-quality datasets, efforts such as [18, 25, 17, 24] have notably enhanced the visual understanding and reasoning capabilities of MLLMs. Additionally, there are works that introduce additional visual encoders [29, 30] or utilize higher-resolution images [25, 18, 24] to provide richer visual signal sources. Meanwhile, many studies [51, 52, 53] directly extend these above image-based methods to video conversational models by leveraging video instruction tuning datasets. In summary, these studies typically utilize high-level features from the frozen visual encoder as visual embeddings. However, we find that effectively leveraging offline features from different layers-the overlooked "free lunch"-can yield significant benefits. Then, we follow FreeVA [54] to directly extend the image model for video understanding without any additional video training.Question: Does the paper discuss the limitations of the work performed by the authors?Answer: [Yes] Justification: We discuss the limitation in the paper.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.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be. • The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. • The authors should reflect on the factors that influence the performance of the approach.For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon. • The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size. • If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness. • While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations. 
overview: 
 In Fig. 2 (a), we illustrate the overall architecture of our model, using the mainstream LLaVA [15] framework as an example. It includes the pre-trained visual encoder V is(•) and the Large Language Model LLM (•), alongside with our proposed Dense Connector DC(•). Similarly, our DC can be seamlessly extended to other high-resolution or dual-branch MLLMs, such as LLaVA-NeXT [25] and Mini-Gemini [18] . Formally, the introduction is as follows:Visual Encoder: We utilize a CLIP pre-trained Vision Transformer (ViT) [21] as the visual encoder for extracting visual features. Initially, ViT partitions an image X i ∈ R H×W ×C into a sequence of . . non-overlapping patches. Each patch is then processed via convolution to produce visual tokens, which are subsequently input into ViT. This procedure yields L layers of visual features V ∈ R L×N ×Dv , where N denotes the number of the visual tokens and D v denotes the feature dimension.Dense Connector: The Dense Connector comprises two components: the first integrates multi-layer visual features, elaborated upon in detail in Sec. 3.2, while the second employs a learnable MLP to map the integrated visual features to the LLM's text space. The MLP consists of two linear layers with a GELU [55] activation function sandwiched between them. The first layer adjusts the visual hidden size D v to align with the LLM's hidden dimension D t , while the second layer maintains the dimensionality at D t . Upon processing through the Dense Connector, we acquire visual embeddings e v ∈ R N ×Dt that encapsulate information from multiple layers.Large Language Model: The LLM processes textual data using a tokenizer and text embedding module to convert language into its input feature space. These text embeddings are concatenated with transformed visual embeddings before being fed into the LLM for subsequent predictions.Here we delve into three intuitive instantiations of the Dense Connector, each demonstrating superior performance compared to the baseline (e.g., LLaVA-1.5 [16] ). Among them, Sparse Token Integration (STI) and Sparse Channel Integration (SCI) sparsely select visual features from K layers (indexed as l n , where 1 ≤ l n < L and 1 ≤ n ≤ K) spanning shallow, middle, and high levels out of the total L layers of ViT, while Dense Channel Integration (DCI) utilizes features from all layers. These features are then fed into Dense Connector to generate visual embedding that can be "understood" by LLM.Sparse Token Integration (STI): While existing methods typically rely solely on features from the final layer as the visual representation input for the LLM, our STI approach diverges from this by integrating features from multiple layers to enrich the visual input for the LLM. Recognizing that higher-level features contain richer semantic information crucial for visual signal perception in VLMs, we maintain the final layer features unchanged while downsampling additional visual features from other layers by using average pooling avg(•) with a stride α. This downsampling reduces the number of visual tokens to N ′ = N/α, mitigating computational overhead and redundancy. These visual features from various layers are concatenated along the token dimension and processed through a shared M LP (•), yielding more robust visual embedding e v ∈ R (N +(k-1)×N ′ )×Dt :EQUATIONSparse Channel Integration (SCI): We delve deeper into connecting multi-level features along the channel dimension. Subsequently, this feature is processed through an MLP projector to obtain visual embedding e v ∈ R N ×Dt :EQUATIONThe MLP projector serves dual functions: integrating various features as a fusion tool and facilitating the transformation of visual inputs into linguistic representations. This design ingeniously leverages the dimensionality scaling effect of the MLP projector, enabling the transformation of connected multilayer features into the feature space of the LLM without requiring additional modules. Moreover, this method does not increase the number of tokens fed into the LLM, thereby avoiding any increase in the computational overhead of the LLM.Dense Channel Integration (DCI): While Sparse Channel Integration incorporates features from K layers, many visual feature from other layers remain unused. Concatenating all visual feature layers using STI or SCI leads to excessively high dimensions, posing challenges during training. To address these issues, we propose DCI, which builds upon the SCI method by integrating adjacent layers to reduce redundancy and high dimensionality. This approach ensures dense connectivity across a wider range of visual layers. Specifically, we partition the features of L layers into G groups, where each group comprises M adjacent visual features, with M = L/G. Summing the features within each group, denoted as GV g , finally yields G fused visual representations:EQUATIONSubsequently, we concatenate these features from the G groups with the final layer's features along the channel dimension before passing them through an MLP:EQUATIONFor MLLMs, each image is converted into hundreds or even thousands of visual tokens, and this large number of tokens increases the computational burden on autoregressive models. However, reducing the number of visual tokens generally leads to a noticeable drop in performance. In this paper, we leverage multi-layer visual features to compensate for the information loss caused by reducing visual tokens, enabling our method to achieve performance on par with LLaVA-v1.5 [16] while using several times fewer visual tokens, and surpassing other carefully designed efficient connectors [24, 38, 50, 56, 57] Specifically, after obtaining the visual embedding e v through the Dense Connector, we apply a parameter-free module, i.e. a 2D interpolation function, to downsample visual tokens. Then, these discrete visual tokens e ′ v are concatenated with the text tokens and fed into the LLM, resulting in a 3 times improvement in inference speed.Following FreeVA [54] , we extend the image-based models trained as described above to video domain for video understanding. Specifically, given a video, we uniformly sample T frames, and each frame is processed through the visual encoder and dense connector to obtain a visual embedding e v . Consequently, we obtain an embedding sequence {e v1 , ..., e v T }, which is then fed into the LLM.Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?Answer: [NA]• It should be clear whether the error bar is the standard deviation or the standard error of the mean. • It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified. • For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates). • If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text. 
implementation details: 
 Architecture. 1) Visual Encoders: To explore the generalization of the Dense Connector across different visual encoders, we select two mainstream options, namely CLIP-ViT-L-336px [8] and SigLIP-ViT-SO [31] . 2) LLMs: The Dense Connector is applied across various LLMs, spanning from 2.7B to 70B parameters. This includes Phi-2-2.7B [44] , Vicuna-7B&13B [12] , Hermes-2-Yi-34B [19] , and Llama3-8B&70B-Instruct. 3) Dense Connector: For the 24-layer CLIP-ViT-L-336px [8] , we specifically target visual features from the 8th, 16th, and final 24th layers for both STI and SCI. For STI, we apply a downsampling factor of α = 8 for the features from the 8th and 16th layers. For DCI, we divide all layer features into two groups, each containing 12 layers (i.e., [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] . Note that we use 1 to denote the stem layer of ViT, e.g., 2-25 correspond to the 24 layers of ViT-L. Similarly, for the SigLIP ViT-SO [31] , which has 27 layers, we partition the first 26 layers into two groups (i.e., [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] .Training Datasets. Data quality plays a crucial role in determining the performance of MLLMs. In this study, we examine the impact of two high-quality training datasets on our model: LLaVA-1.5 [16] and Mini-Gemini [18] . The LLaVA-1.5 pre-training dataset comprises 558K image captions, while its instruction tuning dataset contains 665K conversations. Mini-Gemini builds upon LLaVA-1.5, offering a larger dataset with 1.2M image-text caption pairs for alignment and 1.5M conversations for instruction tuning. Unless otherwise specified, all experimental results are based on the LLaVA-1.5 dataset to reduce training costs.Training Recipe. We train all models on 8 NVDIA A100 GPUs with 40GB VRAM, except for the Hermes-2-Yi-34B and LLama-3-70B-Instruct, which utilize 32 NVDIA A100 GPUs with 80GB VRAM. Our training process comprises two stages: pre-training and instruction fine-tuning. In the pre-training phase, we initialize the visual encoder and LLM with pre-trained weights, while the Dense Connector is randomly initialized. Here, we freeze the visual encoder and the LLM, updating only the parameters of the Dense Connector. The model undergoes pre-training for one epoch with a global batch size of 256 and a learning rate of 1e-3. Subsequently, in the instruction fine-tuning stage, we maintain the visual encoder frozen while updating the Dense Connector and the LLM. Fine-tuning is performed for 1 epoch with a global batch size of 128 and a learning rate of 2e-5. For models using LoRA fine-tuning, we set the LoRA rank to 128 and LoRA alpha to 256. When scaling up the LLM to larger parameter sizes, such as LLama-3-70B-Instruct, we apply LoRA fine-tuning due to memory constraints. In this setup, we set the LoRA rank to 128 and LoRA alpha to 256.Evaluation. We present comprehensive results across various image and video evaluation benchmarks. For image datasets, we include GQA [58] , VQAV2 (VQA v2 ) [59] , ScienceQA (SQA I ) [60] , TextVQA (VQA T ) [61] , POPE [62] , MathVista (Math) [63] , MMBench (MMB) [64] , MM-Vet (MMV) [65] , MMMU [66] , LLaVA-Bench-In-the-Wild (LBW) [15] , and MME [67] . Additionally, we evaluate zero-shot performance on open-ended video question-answering benchmarks such as MSVD-QA [68] , ActivityNet-QA [69] , MSRVTT-QA [70] , and the newly proposed generative performance benchmark [51] , include evaluation metrics such as Correctness of Information (CI), Detail Orientation (DO), Contextual Understanding (CU), Temporal Understanding (TU), and Consistency (CO).Study on Instantiations of Dense Connector. In Tab. 1, we discuss three proposed methods of Dense Connector. 1) Sparse Token Integration (STI): This method uses visual features from different hierarchical levels as independent visual prompts for the LLM, allowing it to perceive a more diverse set of visual features. We select features from the 8th, 16th, and 24th layers, resulting in significant improvements across various datasets, particularly achieving a 2.9% increase on the MMB [64] . Further expanding the selection to include the 8th, 16th, 20th, and 24th layers enhances performance but also increases token count, leading to higher training costs and inference time. 2) Sparse Channel Integration (SCI): This method efficiently uses the MLP projector for feature fusion and projection, integrating multiple layers of visual features into visual embeddings. SCI boosts performance with minimal additional computational cost, achieving peak performance with features from the 8th, 16th, and 24th layers, resulting in a 1.7% improvement on GQA. SCI performs better than STI and mitigates the computational costs associated with higher token counts. However, expanding the range of visual Table 1 : Ablations on Visual Layer Selection in Dense Connector. Here, we explore three instantiations (STI, SCI, and DCI) of our Dense Connector integrated with the baseline (i.e., LLaVA-1.5 [16] ), which utilizes a 24-layer CLIP-ViT-L-336px. underscoring the significant benefits of increased training data. Moreover, when using the same training data and the LLM (i.e., Vicuna-7B), our Dense Connector surpasses the dual encoder structure of Mini-Gemini [18] across the majority of benchmarks. Specifically, it achieves performance gains of 2.4% on the MMB [64] , 4.2% on MM-Vet [65] , and 1.7% on the MathVista [63] benchmark.Study on High-Resolution Setting. The use of high-resolution images to enhance detail representation in MLLMs has garnered considerable attention [25, 18, 26, 27, 28] . In this paper, we extend Dense Connector to the Mini-Gemini (MGM) [18] and LLaVA-NeXT [25] , showcasing its plug-and-play capability. For MGM, we keep the high-resolution features from ConvNeXT [71] intact, applying DCI exclusively to the CLIP features. We observe significant improvements across various benchmarks, as detailed in Tab. 2, including MathVista [63] , MMB [64] , and MM-Vet [65] , with enhancements of 1.1%, 2.2%, and 3.8%, respectively. In addition to the high-resolution architecture of the dual visual encoder, we also extend the Dense Connector to dynamic high-resolution, specifically using the AnyRes technology from LLaVA-NeXT [25] . For a fair comparison, we provide a baseline for LLaVA-NeXT trained on the same dataset. As shown in Tab. 2, Dense Connector achieves overall improvements compared to the dynamic resolution method LLaVA-NeXT as well.Study on Efficient Dense Connector. To achieve faster inference speed, we investigate an efficient Dense Connector in this study, which can accelerate inference by 3 times. As described in Sec. 3.3, we use a 2D bilinear interpolation function to downsample the visual tokens by a factor of 4, reducing the number of tokens from 576 to 144, which decreases the training time during in the second stage on 8 A100 GPUs from 9 hours to 6.5 hours. With the same configuration of using 144 visual tokens, Dense Connector outperforms the carefully designed efficient connector method Tokenpacker [56] by 0.9%, 1.5%, 2.5% and 1.4% on GQA [58] , VQAv2 [59] , MMB [64] , and MM-Vet [65] , respectively.Comparison with SoTAs in Image Understanding. In Tab. 4, we scale the LLMs from 2.7B to 70B parameters and compare them with state-of-the-art MLLMs. When considering lightweight models, our Dense Connector surpasses the previous MLLM, TinyLlava [72] , achieving a 1.7% enhancement on the MM-Vet benchmark using the same fine-tuning data and foundation model. Furthermore, using same training data and LLM, our Dense Connector outperforms the LLaVA-1.5 Vicuna 13B [16] with substantial gains of 2.1%, 3.7%, and 5.5% on the GQA [58] , MMB [64] , and MM-Vet [65] benchmarks, respectively. Notably, even with data solely from LLaVA-1.5, our 13B model achieves performance comparable to MGM [18] , which is trained on larger datasets, including 1.2M+1.5M data. Moreover, utilizing the advanced open-source LLM Llama3-8B-Instruct, our model significantly surpasses LLaVA-LLama3 [76] with improvements of 5.5%, and 52 on MMB [64] , and MME p [67] , respectively, highlighting the contribution of our Dense Connector. By scaling up the LLM to 34B and 70B, Dense Connector achieves further improvements leveraging more powerful language models. The 70B model attains scores of 82.4% on SQA [60] and 79.4% on MMBench [64] . We then increase the resolution using AnyRes technology [25] and fully fine-tuned the LLM. Our 13B model outperforms MGM and LLaVA-NeXT on MMBench [64] and SQA [60] , achieving scores of 72.3% and 72.6%. The 34B model achieves scores of 81.2%, 59.2%, and 97.7% on MMBench [64] , MM-Vet [65] , and LLaVA-Bench-in-the-Wild [16] , respectively. Table 5 : Comparisons with Leading Methods on Zero-shot Video QA Benchmarks. Following FreeVA [54] , we specify the GPT-3.5 versions used for evaluation to ensure fairness in performance comparison across different versions. "MAR" denotes the GPT-3.5-Turbo-0301, "JUN" denotes the GPT-3.5-Turbo-0613, and "JAN" denotes the latest GPT-3.5-Turbo-0125. Comparison with SoTAs in Video Understanding. Building on the training-free paradigm of FreeVA [54] for image-to-video adaptation, we directly apply our models, originally trained on image-text datasets, to video dialogues. As indicated in Tab. 5, the visual enhancement capabilities of the Dense Connector significantly enhance the video comprehension of our 13B model. This model surpasses the baseline LLaVA-1.5 [16] with FreeVA [54] on MSVD [68] and ActivityNet [69] . By scaling up the LLM to 34B, our model demonstrates a marked improvement in video understanding capabilities, achieving state-of-the-art performance on video evaluation benchmarks with accuracies of 77.4% on MSVD [68] , 62.1% on MSR-VTT [70] , and 55.8% on ActivityNet [69] . (f) Video Reasoning Qualitative Results. In Fig. 3 , we illustrate our model's exceptional visual understanding and text generation capabilities across various scenarios, encompassing both image and video. More qualitative results are provided in Appendix A.5. 
conclusion and limitation: 
 In this paper, we introduce the Dense Connector, a novel plug-and-play module that enhances visual perception capabilities of MLLMs by densely integrating multi-layer visual features. We instantiated three types of Dense Connector and validate the efficacy of it across a diverse array of vision encoders, LLMs, and training datasets, demonstrating substantial improvements in performance across multiple evaluation benchmarks. Dense Connector can be easily integrated into existing MLLMs. In this work, we incorporate the Dense Connector into mainstream model LLaVA and high-resolution method Mini-Gemini, demonstrating its versatility and generalization capabilities.Limitations: Our three Dense Connector instantiations do not introduce additional parameters, leaving room for further exploration. We have not yet found an effective method for incorporating additional parameters. Future research will focus on discovering more efficient ways to connect visual and language models for better modality alignment. 
experiments compute resources: 
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broader impacts: 
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safeguards: 
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 Appendix: In Sec. 3.2, we provide a detailed discussion of three instantiation methods of the Dense Connector: Sparse Token Integration (STI), Sparse Channel Integration (SCI), and Dense Channel Integration (DCI). Applying our proposed Dense Connector to representative VLMs (e.g., LLaVA [16] ) results in notable performance improvements. In this section, building on the STI, SCI, and DCI methods, we explore instantiations of Dense Connectors with additional learnable parameters.Sparse Token Integration with 1D Convolutional Downsampling. The STI instantiation method concatenates visual tokens from various layers, which significantly increases the total number of tokens. To reduce redundancy and computational overhead, we initially employ average pooling to reduce the number of tokens from shallow layer features. In computer vision, using convolution for downsampling is a common practice [84] . Here, we replace average pooling with a single 1D convolutional layer Conv 1D (•), to downsample the shallow layer tokens. We set both the kernel size and stride to 8, maintaining consistency with the average pooling as detailed in Sec. 4.1. The formula is as follows:EQUATIONSparse Channel Integration with 2D Convolutional Modelling. The SCI method utilizes the projector as both a feature fusion tool and a modality mapper, transforming multi-layer visual features concatenated along the channel dimension into the input feature space of the LLMs. By straightforwardly concatenating multi-layer visual features, the SCI method achieves significant performance enhancements for VLMs with minimal computational overhead. As a central element of the Dense Connector, we aim to enhance local perception abilities by processing visual features through a 3×3 2D convolution Conv 2D , with shared weights prior to feature concatenation:EQUATIONDense Channel Integration with Linear Layer. The DCI method initially groups visual features and then aggregates them, enabling the fusion of adjacent visual features without expanding the dimensionality. This technique mitigates the issues of feature redundancy and excessive channel dimensions that arise in the SCI method from incorporating too many layers. To enhance the integration of features from different visual layers across groups, each layer of visual features is processed through a linear layer with shared weights. The formula is as follows:EQUATIONEQUATIONwhere Ln(•) and Linear(•) denotes Layer Normalization and Linear layer, respectively.Experimental Results and Analysis. We present the detailed results of these described instantiations in Tab. 6. 1) For the STI method, using features from the 8th, 16th, and 24th layers, average pooling demonstrates superior performance compared to 1D convolutional downsampling, especially on the GQA [58] and MMBench [64] benchmarks. 2) In the SCI method, applying 2D convolution to enhance local feature modeling with offline ViT features from the 8th, 16th, and 24th layers achieves comparable performance to methods without 2D convolution. 3) Furthermore, incorporating additional aggregation information, such as a linear layer, into the DCI method does not lead to improved outcomes.In summary, our attempts to introduce additional parameterized modules did not yield the anticipated improvements. We hypothesize that since the connector is randomly initialized, maintaining ease of convergence is crucial for effectively training the connector to align visual and language models under limited training data. In current MLLMs, the MLP structure of LLaVA [16] efficiently facilitates convergence between visual and language components. However, adding extra parameters may disrupt this inherent characteristic, leading to diminished performance. Future research will explore more complex and effective implementations of the Dense Connector.In Appendix A.1, we further explored more complex Dense Connector structures. However, we found that non-parameterized methods yielded better average results. Therefore, we also visualized the comparison of three main instantiations of the Dense Connector with LLaVA-1.5 in Fig. 4 , clearly demonstrating DCI's superior performance across different benchmarks. To probe the upper limits of MLLMs, we attempt to utilize a larger dataset to fine-tune our model based on Llama3-70B-Instruct. Given the limitations of computational resources, we commence with the Dense Connector pre-trained on LLaVA's initial stage data [16] and subsequently refine it using the instructional dataset from Mini-Gemini [18] . This model achieves accuracy of 40.7% on the MathVista [63] and 53.3% on MM-Vet [65] , as illustrated in Tab. 7. Remarkably, our 70B model exhibits performance that is on par with GPT-4V on the LLaVA-Bench-in-the-Wild evaluation [16] . Specifically, our model achieves 97.8%, closely trailing GPT-4V's 98% [85] . However, it is worth noting that the full potential of our 70B model remains untapped due to the lack of initial stage pre-training data and the inherent constraints of LoRA fine-tuning [86] .In the experiments above, we used frozen multi-layer features from a pre-trained ViT to enhance the model's visual perception capabilities. Recently, however, there has been a growing trend of fine-tuning the entire model, including ViT, during the instruction-tuning phase to achieve better results. Following this trend, we present additional results in Tab. 8 on the performance impact of finetuning the ViT within the multi-layer visual connector. Specifically, in the first stage, we maintain the original training strategy. In the second stage, we fine-tune the ViT with a smaller learning rate of 2e-6, keeping other hyperparameters unchanged. Tab. 8 shows that fine-tuning ViT yields performance gains on benchmarks with a higher reliance on visual information, such as MMBench [64] and TextVQA [64] . For instance, fine-tuning CLIP-Large encoder resulted in improvements of 1.8% and 1.0% on MMBench and TextVQA, respectively. Conversely, on benchmarks that are less visually dependent, such as SQA [60] , performance exhibited a slight decline.From Fig. 6 to Fig. 13 , we present additional multimodal dialogue illustrations with images and videos. Fine-tuned on image-text datasets, Dense Connector achieves effective alignment between visual and linguistic modalities, enhancing the model's visual perception capabilities. Qualitative evaluations showcases that our model can adeptly handle a broad spectrum of challenges across various domains, including mathematics, flowcharts, and daily scenarios. Additionally, the incorporation of the FreeVA method [54] equips our model with video understanding capabilities, enabling our model to address dynamic temporal challenges. However, due to the lack of training on video data, our model may occasionally misinterpret images as videos. For instance, as shown in Fig. 12 , the model mistakenly interprets videos as images, incorrectly responding with 'The image' instead of 'The video'.What is the solution if the lamp is plugged in and the bulb has burned out?Which step in the flowchart is drawn with a diamond shape?The step in the flowchart that is drawn with a diamond shape is "Water boiled?" This is a decision point in the flowchart, where the process can continue in two different directions based on the outcome of the condition being tested.The solution if the lamp is plugged in and the bulb has burned out, as indicated by the flowchart, is to "REPLACE BULB."What is the step after using probe to spread tire puncture?After using the probe to spread the tire puncture, the next step is to remove the repair plug from the plastic. This is shown in step 5 of the guide, where the hands are holding the repair plug and the plastic, preparing to separate them. What is the answer to the bottom left equation?Dense ConnectorThe answer to the bottom left equation, which is "9 + 3 =", is 12.What is the answer to the second equation on the right?The answer to the second equation on the right, which is "6 + 1 =", is 7. Therefore, the elastic band has been stretched from its original length of 16 cm to a total length of 20 cm, which means it has been stretched by 4 cm. Can you help me understand or explain a meme? Please describe the meme or provide details about its context.Please explain the meme in detail.The image is a humorous take on a McDonald's meal, with a sandwich and fries made out of fruits and vegetables. The sandwich is made from a cut apple, and the fries are made from carrot sticks. The arrangement is placed on a dining table, and the words "Nice Try Mom" are written above the image. The meme is poking fun at the idea of a mother trying to make a healthy version of a fast-food meal, but the result is not quite convincing. The phrase "Nice Try Mom" is a lighthearted way of acknowledging the effort while also implying that the outcome is not entirely successful.The meme features a photograph of a small, light-colored dog lying on its side on a wooden floor. The dog's posture is relaxed, with its head resting on the floor and its body stretched out. The image is overlaid with text in a bold, white font that reads "MONDAY, JUST... MONDAY." The text is a play on the common sentiment that Mondays are often dreaded as the start of the workweek, and the dog's posture humorously suggests a reluctance to get up and face the day, which is a feeling many people can relate to. The meme uses the dog's expression and body language to anthropomorphize the animal, attributing human emotions and behaviors to it, which is a common technique in meme creation to evoke humor and empathy.", What is the name of this dish, and how is it made?How to cook this dish?To cook this dish, which appears to be a type of sushi roll, you would typically follow these steps: 1. Prepare the sushi rice: Rinse the rice thoroughly and cook it according to the package instructions. Once cooked, allow it to cool. Mix the rice with rice vinegar, sugar, and salt to give it the characteristic sushi flavor. 2. Prepare the filling: Slice the ingredients you want to include in the roll, such as cucumber, avocado, and imitation crab, into thin strips. 3 . Assemble the roll: Lay a sheet of nori seaweed flat on a sushi mat. Spread a thin layer of sushi rice onto the seaweed, leaving a small border at the top.Arrange the filling ingredients horizontally in the middle of the rice. 4. Roll the sushi: Using the sushi mat, roll the nori and rice tightly but gently, applying even pressure. Make sure the roll is tight and the ingredients are evenly distributed. 5 . Slice the roll: Once the roll is formed, place it in the refrigerator for about 30 minutes to set. Then, using a sharp knife, slice the roll into individual pieces.\n\n6. Serve: Serve the sushi with soy sauce, wasabi, and pickled ginger, as shown in the image. The yellow condiment could be grated daikon or a similar garnish. Remember to handle the sushi rice and ingredients with clean hands or utensils to maintain hygiene. Enjoy your homemade sushi!The dish in the image is commonly known as "Mapo Tofu," a traditional Sichuan dish. It is made by first marinating and then pan-frying tofu cubes until they are lightly browned. The sauce is prepared separately by stir-frying a mixture of ground pork, garlic, ginger, and fermented soybeans in chili oil. The sauce is then seasoned with soy sauce, rice vinegar, and cornstarch to thicken it. The tofu is then added to the sauce and cooked until it is well coated. The dish is garnished with green onions and served hot. Which movie is this image from?What's the ending of this movie?The image appears to be a still from the movie "Pulp Fiction" (1994) directed by Quentin Tarantino. The character depicted is Mia Wallace, played by actress Uma Thurman. This particular scene is memorable for its iconic dialogue and the character's distinctive style, which has become a cultural reference point.The image captures a moment of Mia Wallace's character in a diner, which is a pivotal scene in the film.The significance of this scene in the context of the movie is multifaceted. It represents a pivotal moment of freedom and joy for the characters, who are from different social classes and are experiencing a moment of escape from their societal constraints. The act of standing at the bow of the ship with arms outstretched is a classic symbol of liberation and the pursuit of dreams. It also serves as a visual metaphor for the characters' aspirations and the limitless possibilities that lie ahead, both literally as they travel across the ocean and metaphorically in their personal lives. This scene is often associated with a sense of hope and the beginning of a new chapter, which is a central theme in the narrative of the film.What is the significance of this scene in the context of the movie?The ending of the movie \"Titanic\" is a tragic one. The film is based on the real-life story of the RMS Titanic, which sank on its maiden voyage in 1912. The movie's plot follows the fictional characters Jack Dawson and Rose DeWitt Bukater, who fall in love aboard the ship during its ill-fated journey. The ship's collision with an iceberg and subsequent sinking lead to the death of Jack, who sacrifices his life to save Rose. The film ends with an elderly Rose, who has been telling the story of her time on the Titanic to her granddaughter and a group of explorers, throwing the \"Heart of the Sea\" diamond into the ocean, finally letting go of the past. The ending is poignant, reflecting on the themes of love, loss, and the passage of time. Describe the video in detail.The video depicts a person drawing a tree with pink flowers on a white canvas. The artist is using a brush to create the intricate details of the tree, which is surrounded by a large circle. The tree appears to be a cherry blossom tree, as it is covered in pink flowers. The artist's hand is visible, holding the brush and working on the painting. The scene captures the creative process of the artist as they bring the tree to life on the canvas.In the midst of a tranquil scene, A tree stands tall, adorned with pink leaves, Its branches reaching out, As if to embrace the world. A hand, holding a brush, Paints the tree with care and grace, Creating a masterpiece, A reflection of nature's beauty. The artist's skillful strokes, Bring life to the canvas, A tree in bloom, A symbol of growth and renewal. The moon, a gentle companion, Shines above the tree, Guiding the artist's hand, As the painting takes shape. A moment captured in time, A tree in bloom, a hand at work, A testament to the power of art, To evoke emotions and inspire.Compose a poem inspired by this artwork.Describe the video in detail.The video depicts a group of young men gathered around a dining table, engaged in a friendly arm wrestling competition. There are at least five people in the scene, with two of them actively participating in the arm wrestling match. The other individuals are watching the competition, likely cheering on their friends or waiting for their turn.The table is cluttered with various items, including a bottle and a cup placed near the center of the table. The atmosphere appears to be lively and competitive, as the young men enjoy their time together. Describe the video in detail.Dense ConnectorThe video features a young child sitting on a bed, wearing glasses and holding a book. The child appears to be reading or looking at the book, possibly enjoying a story or learning something new. The bed is covered with a blanket, providing a comfortable and cozy environment for the child to engage in their reading activity.The video is funny because it features a baby wearing glasses and holding a book, pretending to read. The baby's actions and the fact that it is wearing glasses, which are typically associated with adults, create a humorous and adorable scene. The baby's innocent and curious expression as it looks at the book adds to the humor and charm of the moment. The combination of the baby's actions and the visual of a young child wearing glasses creates a lighthearted and entertaining scene that can bring a smile to the viewer's face.Why is this video funny?What is strange about this video?The strange aspect of this video is that a man is sitting in the middle of a group of lions, which is an unusual and potentially dangerous situation. Lions are wild animals and are known for their strength and predatory instincts. It is highly unusual for a person to be in such close proximity to a group of lions without any protective measures or barriers. The man's presence among the lions raises concerns about his safety and the potential risks involved in such an encounter. What is the video about?Dense ConnectorThe image features a woman sitting at a table, holding a tube of sunscreen in her hand. She is applying the sunscreen to her face, ensuring that she is protected from the sun's harmful rays. The sunscreen is a product from Skin Authority, and the woman is using it to moisturize her skin and maintain its health. The scene emphasizes the importance of using sunscreen as a daily skincare routine to prevent sun damage and maintain a healthy complexion.Based on the image, the product featured is a tube of sunscreen moisturizer from Skin Authority. Some similar products that you might consider are other sunscreen moisturizers from reputable brands, such as Neutrogena, Aveeno, or CeraVe. These brands offer a range of sunscreen moisturizers with varying SPF levels and formulations to cater to different skin types and preferences.It is essential to choose a product that suits your skin type and provides adequate protection against harmful UV rays while moisturizing your skin.Could you recommend some products similar to the one featured in the video?Can you identify any safety hazards in this video?In the video, there is a baby playing with a toy truck near a fireplace. This situation presents a potential safety hazard, as the baby might accidentally fall into the fireplace or get too close to the fire, which could lead to burns or other injuries. It is important for parents or caregivers to supervise the child closely and ensure that the baby is playing in a safe distance from the fireplace to prevent any accidents. Describe the video.Dense ConnectorThe image features a person standing in a kitchen, preparing a meal by cutting up a variety of bell peppers. The person is holding a knife and is in the process of slicing the peppers into smaller pieces. The sliced peppers are placed on a tray, with some of them already cut and others still needing to be prepared. The scene captures the person's attention to detail and focus on preparing the meal.There are four slices of red peppers on the baking sheet.How many slices are on the baking sheet.Describe the video.The image captures a snowy day at a track and field event, where a man is preparing to throw a javelin. He is standing on a red track, wearing a blue shirt and black pants, and is focused on his performance. There are several other people in the scene, some of whom are standing near the track, possibly watching the event or waiting for their turn.In addition to the main javelin thrower, there are two other people visible in the image, one on the left side and another on the right side of the scene. The presence of multiple people suggests that this is a competitive event or a practice session with multiple participants. Justification: We provide our contributions both in the abstract and introduction.Dense ConnectorGuidelines:• 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.Justification: There is no societal impact of the work performed.Guidelines:• The answer NA means that there is no societal impact of the work performed.• If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact. • Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.• The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. • The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology. • If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML). 


Decision: This work intoduced dense connector for multimodal LLMs to enhance to the visual percetion capability, akin to densenet or FPN in vision models. The method is simple yet effective across the board, spanning from different benchmarks with different resolutions and variou LLM sizes. The paper is also well-written and method is clearly motivated. During the discussion, the authors and reviewers were engaged in intensive discussions. The authors provided a detailed and constructive rebuttal, addressing all reviewer concerns effectively. As a result, it recevied all positive final ratings (5, 6, 7). 

Overall, this work presents a technically sound contribution to the field of MLLMs. According to the submission and discussions, the proposed Dense Connector is a versatile module that offers consistent performance improvements with minimal additional computation. While some concerns were raised about the incremental nature of the gains and the generalizability of the approach, the authors’ thorough rebuttal addressed these issues convincingly. As a result, the ACs recommend an acceptance, and suggest the authors integrate the additional experimental results and analysis during rebuttal to the final version, and release the implementation code to public for benefit to the community.

Reviews: review: paper_summary: paper_summary: This paper introduces the Dense Connector, a simple idea that aligns visual and language modalities by utilizing multi-layer visual features. The authors explore three instantiation methods and demonstrates the effectiveness of the Dense Connector across various settings, including different backbones, modalities, datasets and architectures.
strength_weakness: strength_weakness: 1.	High-resolution approaches are widely utilized in vision-language models. This paper explores the application of the Dense Connector with dual visual encoders, specifically Mini-Gemini, in high-resolution settings. However, it lacks an exploration of dynamic high-resolution scenarios, such as those implemented by LLaVA-NEXT. I am curious about whether the Dense Connector could further enhance MLLM performance in this scenario.
2.	My another concern is that although the Dense Connector performs well on the Vicuna 7B and 13B models, its effectiveness seems to diminish when scaling to larger sizes. I suspect this might be because training LoRA is not as effective as updating the LLM. If possible, the authors should provide results for updating the LLM to give readers a better reference.
questions: questions: Beyond the section on weaknesses, I have the following additional questions:
1.	The decision to fine-tune ViT during MLLM training is still debated. Research findings are mixed; some [1] indicate that fine-tuning ViT may impair performance, while others [2] show it can lead to performance improvements. I wonder whether the Dense Connector, which utilizes multi-layer visual features, could benefit from fine-tuning ViT.
2.	The authors could specify the additional training and testing time required by the Dense Connector.
[1] Karamcheti, Siddharth, et al. "Prismatic vlms: Investigating the design space of visually-conditioned language models." arXiv preprint arXiv:2402.07865 (2024).
[2] Chen, Lin, et al. "Sharegpt4v: Improving large multi-modal models with better captions." arXiv preprint arXiv:2311.12793 (2023).
limitations: limitations: The authors have addressed the limitations.
paper_summary: paper_summary: This paper endeavors to delve into the visual representations in MLLMs, introducing a module, plug-and-play component named as Dense Connector (DC). This  DC is designed to enhance visual representation. To this end, three  instantiations are presented, including Sparse Token Integration(STI), Sparse Channel Integration(SCI), and Dense Channel Integration(DCI).  Thorough experimental evaluations are meticulously carried out to validate the compatibility and scalability, thereby highlighting their potential in enhancing MLLM performance.
strength_weakness: strength_weakness: 1. The insight of enhancing visual representations from multi-level features of vision encoder have been explored in previous works, like [1]. The work [1]  has provided the simliar evidences from multi-level features, and more comprehensive analysis based on a amout of experimetal results.
Would authors provides any possible fair comparsions with  the work[1] (Only CLIP vision encoder)?

   [1] Jiang et al.,  From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models, Arxiv 2023.

2. While this work outlines three architectural configurations for the Dense Connector (DC),  it primarily employs a single variant – the Dense Channel Integration (DCI). It is important to note that the DC module serves more as an empirical trick rather than a theoretical methodology.

3.  I think the performance gain is limited, especially on high-resolution MGM baseline in Table2. The  scalability of DC may be limited.
 To comprehensively assess its effectiveness, I encourage the authors to extend their comparisons utilizing a Vicuna-13B model based on the MGM framework, incorporating additional benchmark datasets such as VQAv2, MME, and POPE. It is speculated that the incremental performance gain might become even less pronounced when employing larger language models (LLMs),
questions: questions: It would be beneficial to include a line chart that incorporates a broader range of combinations, facilitating a clearer visual comparison and enabling the selection of groups more evidently.
limitations: limitations: Suggestions: The presented DC can be furthre to combine multiple vision encoder, like DINOv2.
paper_summary: paper_summary: The document presents a novel approach called the Dense Connector (DC), which is a simple and effective plug-and-play vision-language connector that enhances existing Multimodal Large Language Models (MLLMs) by leveraging multi-layer visual features from the frozen visual encoder.The authors propose three intuitive instantiations of the Dense Connector.The authors demonstrate the versatility and scalability of their approach across various visual encoders, image resolutions, training dataset scales, LLM sizes, and MLLM architectures, achieving state-of-the-art performance on 19 image and video benchmarks.
strength_weakness: strength_weakness: 1. authors do experiments on several MLLM models, however don't summarize the compatible scope of models architectures.
questions: questions: I am curious whether the DC is compatible with BLIP2-like architectures which have cross-attention visual resamplers?
Could authors give generalization of the compatible types of MLLM architecture?
limitations: limitations: authors adequately addressed the limitations

Comments: title: Response to reviewer 2pj5
Response to reviewer qnn9
Response to reviewer 2pj5
comment: We are very grateful for the reviewer's hard work. We believe that the reviewer's feedback has greatly refined our paper.

We will revise the paper according to the reviewers' suggestions and **incorporate the aforementioned results into the main body of the paper.**
Thank you for the reviewer's comments. 

We used a visual resampler based on **Flamingo** [1], with 64 query tokens, 6 layers, a hidden size of 1024, and 16 heads.
In the first stage, we fine-tune the visual resampler and the linear layer that follows it, while freezing the ViT and LLM. In the second stage, we fine-tune the visual resampler, the linear layer, and the LLM, keeping only the ViT frozen.

Thank you very much for your time and comments! Please let us know if there are any further questions that need clarification.

[1] Alayrac, Jean-Baptiste, et al. "Flamingo: a visual language model for few-shot learning." Advances in neural information processing systems 35 (2022): 23716-23736.
Thank you for the author's feedback.  
I want to know, what are the models with visual resampler used in your experimental results above?
Thanks for the efforts for the further response.

The results you've presented above thus far are convincing, and I think that they will enhance the robustness of the proposed method.

The detailed and comprehensive comparisons above on the MGM-7B/13B, as well as its contrast with COMM, furnishes a validation of the approach's efficacy.  I think $\textbf{these results should be incorporated into the main body of the paper}$ to ensure that the readers are fully apprised of this work.

I lean to the positive  and raise my score.
Thank you for the reviewer's comments. Due to the word limit, we only provided a portion of the benchmark results in our previous response. **We now offer a more comprehensive performance comparison.**

**Q1.More comparisons with MGM**

Thank you for your feedback. We agree that adding more benchmark comparisons will be helpful for this paper.

We provide further comparisons between MGM-7B and MGM-13B here. According to the table below, DC improved performance across these benchmarks, **with an average increase of 1.1 on MGM-7B and 1.9 on MGM-13B.**

|     | VQAv2 | MMBench | POPE | MME | LLaVA-Bench | MM-Vet | GQA | SQA | TextVQA | MMMU | MathVista | AVG |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| MGM-7B | 80.4 | 69.3 | 85.7 | 1523/316 | 85.4 | 40.8 | 62.6 | 70.4 | 65.2 | 36.1 | 31.4 | 65.4 |
| DC-7B  w/ MGM | 81.1 | 70.7 | 86.1 | 1530/347 | 88.7 | 42.2 | 63.3 | 70.7 | 66.0 | 36.8 | 32.5 | 66.5 (**+1.1%**) |

|     | VQAv2 | MMBench | POPE | MME | LLaVA-Bench | MM-Vet | GQA | SQA | TextVQA | MMMU | MathVista | AVG |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| MGM-13B | 80.8 | 68.5 | 85.1 | 1565/322 | 87.5 | 46.0 | 63.4 | 72.6 | 65.9 | 38.1 | 37.0 | 67.2 |
| DC-13B  w/ MGM | 81.9 | 70.7 | 85.6 | 1573/355 | 92.0 | 49.8 | 64.2 | 74.9 | 66.7 | 39.3 | 38.1 | 69.1(**+1.9%**) |

In current MLLMs, no matter which model we consider, there are inevitably instances where performance gains on certain benchmarks are minimal. Our experiments trained various models across different settings to validate the generalization of DC, including different vision encoders, LLMs, training data, modalities, and architectures. Given the diversity of settings, it's understandable that some results may appear less pronounced.

We provided **official results** to show that even representative models (LLaVA, MGM) are unable to achieve significant improvements across all benchmarks. For example, in LLaVA-1.5, both the 7B and 13B models achieved the **same score of 85.9 on POPE**. In LLaVA-Next, the 7B to 13B models only improved by **+0.7** on POPE, **+0.7** on MathVista, and **+0.6** on MMMU. And **expanding LLaVA-1.5 13B to the high-resolution LLaVA-NeXT yielded only a 0.3 improvement on POPE.** Similarly, in MGM, when extended to higher resolution, MGM-13B showed improvements of **+0.1** on MMBench, **-0.8** on MMMU, and **+0** on Math.

We want to convey that, like representative models in MLLM such as LLaVA and MGM, even **they cannot achieve consistent improvements across so many benchmarks**.

We analyzed that, on one hand, the POPE is **relatively simple**, with results already being high, making further improvements more difficult. Additionally, POPE evaluates the model's ability to handle hallucinations, and since neither LLaVA nor DC is specifically designed to address this, the gains on POPE are both limited. On the other hand, benchmarks like SQA and GQA often **rely more on language capabilities than visual signals**. These benchmarks don't require strong visual perception to perform well on VQA tasks. For instance, **the test question in SQA below can be answered without any visual input.** Since DC is designed to enhance visual perception, its impact on SQA and GQA may be limited in these cases.

> SQA: "question_id": "2828", "prompt": "<image>\nWhat is the capital of Iowa?\nA. Davenport\nB. Helena\nC. Lansing\nD. Des Moines\nAnswer with the option's letter from the given choices directly."

DC has demonstrated noticeable performance gains across most benchmarks, achieved with almost **no additional overhead.** For benchmarks that **rely more on visual perception**, such as VQAv2, MM-Vet, and MMBench, DC improved MGM-13B by **1.1, 3.8, and 2.2**, respectively.

**Q2.More comparisons with COMM**

Thank you for your suggestion! Adding more benchmark comparisons will further strengthen this paper.

We offer additional benchmark comparisons with COMM, showing that DC's average performance improved by **+1.4% over LLaVA** and by **+1.7% over COMM.**

|     | TextVQA | MME | SQA | POPE | LLaVA-Bench | GQA | VQAv2 | MM-Vet | MM-Bench | MathVista | AVG |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| LLaVA (baseline) | 58.2 | 1511 | 66.8 | 85.9 | 65.4 | 61.9 | 78.5 | 31.1 | 64.3 | 24.9 | 61.3 |
| LLaVA+COMM | 58.0 | 1493 | 67.4 | 86.0 | 60.5 | 62.9 | 79.2 | 30.1 | 65.7 | 25.8 | 61.0 **(-0.3%)** |
| LLaVA+DC | 59.2 | 1511 | 69.5 | 86.6 | 66.1 | 63.8 | 79.5 | 32.7 | 66.8 | 26.9 | 62.7 **(+1.4%)** |

We hope this result convinces the reviewer that DC is robust across various benchmarks and outperforms other similar methods.

Thank you very much for your time and comments! Considering that the deadline for discussion is approaching, please let us know if there are any further questions that need clarification.
Thank you for the author's feedback.

I recognize that there may have been some misunderstandings in my previous questions. To clarify, in addition to the original benchmarks, please include detailed comparisons for VQAv2, MME, POPE and etc  based on the MGM-13B framework.  Please further incorporate the original GQA, SQA^I, VQA^T, MMMU^v, and Math, as presented in Table 2 of the main paper.   Because I noticed a slight performance improvement on primary benchmarks: SQA^I (+0.3), GQA (+0.7) with the MGM-7B model, along with the POPE result (+0.5) on the MGM-13B.  Besides, I also noticed the performance gain of GQA (+0.6) on LLaVA-NeXT. 

Moreover, it would be beneficial to further provide detailed comparisons (approximately 10 benchmarks) with COMM, akin to extending the second table included in your response.

I believe that a foundational module for MLLM should be robust enough to accommodate a variety of benchmarks while outperforming other similar methods.