MG-LLaVA 是一种创新的多模态大型语言模型 (MLLM)，它通过整合多粒度视觉流来显著改善视觉处理能力。它包含低分辨率、高分辨率和以对象为中心的特征，增强了模型捕捉细粒度细节和改进对象识别的能力。MG-LLaVA 框架建立在 LLaVA 的架构之上，该架构集成了高分辨率视觉编码器、用于特征整合的 Conv-Gate 融合网络以及从由开放词汇检测器识别出的边界框中提取的对象级特征。

🤔 MG-LLaVA 是一种创新的多模态大型语言模型 (MLLM)，旨在解决当前多模态大型语言模型 (MLLM) 在处理低分辨率图像方面的局限性。它通过整合多粒度视觉流，包括低分辨率、高分辨率和以对象为中心的特征，来实现这一目标。 这种多粒度方法增强了模型捕捉综合视觉细节并将其与文本嵌入整合的能力，从而提高了视觉理解能力。MG-LLaVA 框架建立在 LLaVA 的架构之上，该架构集成了高分辨率视觉编码器、用于特征整合的 Conv-Gate 融合网络以及从由开放词汇检测器识别出的边界框中提取的对象级特征。 MG-LLaVA 框架利用 CLIP 预训练的视觉 Transformer (ViT) 处理低分辨率特征，并利用 CLIP 预训练的 ConvNeXt 处理高分辨率特征。Conv-Gate 融合网络对齐特征的通道宽度并调节语义信息，从而有效地融合这些特征，同时保持计算效率。

😊 MG-LLaVA 通过引入一个多粒度视觉流，有效地处理低分辨率、高分辨率和以对象为中心的特征，从而解决了当前 MLLM 的局限性。这种创新方法显著增强了模型的视觉感知和理解能力，在各种多模态基准测试中表现出优异的性能。 研究人员在多个基准测试（包括 MMBench 和 SEEDBench）上进行了广泛的评估，结果表明，MG-LLaVA 在参数规模相当的情况下，优于现有的 MLLM。该模型显著提高了感知和视觉理解能力，超过了 GPT-4V 和 GeminiPro-V 等模型。该研究还包括全面的消融实验，证实了对象级特征和 Conv-Gate 融合网络的有效性。

😄 MG-LLaVA 架构包括两个关键组件：多粒度视觉流框架和大型语言模型。视觉流框架以不同分辨率处理图像，使用 CLIP 预训练的视觉 Transformer (ViT) 处理低分辨率特征，并使用 CLIP 预训练的 ConvNeXt 处理高分辨率特征。为了有效地融合这些特征，Conv-Gate 融合网络对齐特征的通道宽度并调节语义信息，同时保持计算效率。 对象级特征是使用感兴趣区域 (RoI) 对齐来整合的，从识别出的边界框中提取详细特征，然后与其他视觉标记连接起来。这种多粒度方法增强了模型捕捉综合视觉细节并将其与文本嵌入整合的能力。MG-LLaVA 在公开可用的多模态数据上进行训练，并使用视觉指令调优数据进行微调。

Multi-modal Large Language Models (MLLMs) have various applications in visual tasks. MLLMs rely on the visual features extracted from an image to understand its content. When a low-resolution image containing fewer pixels is provided as input, it translates less information to these models to work with. Due to this limitation, these models often need to be more accurate to identify the objects, scenes, or actions in the image. This behavior of MLLMs affects their effectiveness in visual tasks.

Researchers from the Shanghai Jiaotong University, Shanghai AI Laboratory, and S-Lab, Nanyang Technological University have introduced a novel MLLM model, MG-LLaVA to address the limitations of current Multi-modal Large Language Models (MLLMs) in processing low-resolution images. The key challenge lies in enhancing these models to capture and utilize high-resolution and object-centric features for improved visual perception and comprehension.

Current MLLMs typically use pre-trained Large Language Models (LLMs) to process concatenated visual and language embeddings, with models like LLaVA adopting low-resolution images as inputs. While these models have shown promise, they rely on low-resolution inputs limiting their ability to process fine-grained details and recognize small objects in complex images. Researchers have proposed various enhancements to address this, including training on diverse datasets, using high-resolution images, and employing dynamic aspect ratios. However, these approaches often need the integration of object-level features and multi-granularity inputs, which are crucial for comprehensive visual understanding.

The proposed model, MG-LLaVA is an innovative MLLM that significantly improves visual processing by incorporating a multi-granularity vision flow. This includes low-resolution, high-resolution, and object-centric features, enhancing the model’s ability to capture fine-grained details and improve object recognition. The MG-LLaVA framework builds on the architecture of LLaVA that integrates a high-resolution visual encoder, a Conv-Gate fusion network for feature integration, and object-level features derived from bounding boxes identified by open-vocabulary detectors.

The MG-LLaVA architecture comprises two key components: the Multi-Granularity Vision Flow framework and a large language model. The Vision Flow framework processes images at different resolutions, using a CLIP-pretrained Vision Transformer (ViT) for low-resolution features and a CLIP-pretrained ConvNeXt for high-resolution features. To fuse these features effectively, the Conv-Gate fusion network aligns the features’ channel widths and modulates semantic information, maintaining computational efficiency.

Object-level features are incorporated using Region of Interest (RoI) alignment to extract detailed features from identified bounding boxes, which are then concatenated with other visual tokens. This multi-granularity approach enhances the model’s ability to capture comprehensive visual details and integrate them with textual embeddings. MG-LLaVA is trained on publicly available multimodal data and fine-tuned with visual instruction tuning data.

Extensive evaluations across multiple benchmarks, including MMBench and SEEDBench, demonstrate that MG-LLaVA outperforms existing MLLMs of comparable parameter sizes. The model significantly improves perception and visual comprehension, surpassing models like GPT-4V and GeminiPro-V. The study also includes comprehensive ablation experiments, confirming the effectiveness of the object-level features and Conv-Gate fusion network.

In conclusion, MG-LLaVA addresses the limitations of current MLLMs by introducing a multi-granularity vision flow that effectively processes low-resolution, high-resolution, and object-centric features. This innovative approach significantly enhances the model’s visual perception and comprehension capabilities, demonstrating superior performance across various multimodal benchmarks.

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The post MG-LLaVA: An Advanced Multi-Modal Model Adept at Processing Visual Inputs of Multiple Granularities, Including Object-Level Features, Original-Resolution Images, and High-Resolution Data appeared first on MarkTechPost.