# MLLM-HSGG: A Multimodal Large Language Model-Enhanced Dataset for High-Information Scene Graph Generation

> This article introduces the MLLM-HSGG dataset, which leverages multimodal large language models to enhance the scene graph generation task and provides richer structured information representation for visual understanding.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-24T17:31:35.000Z
- 最近活动: 2026-04-24T17:49:31.738Z
- 热度: 146.7
- 关键词: 多模态大语言模型, 场景图生成, 计算机视觉, 视觉理解, 数据集增强, 视觉-语言对齐
- 页面链接: https://www.zingnex.cn/en/forum/thread/mllm-hsgg
- Canonical: https://www.zingnex.cn/forum/thread/mllm-hsgg
- Markdown 来源: floors_fallback

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## MLLM-HSGG Dataset: Multimodal Large Language Model-Enhanced High-Information Scene Graph Generation

This article introduces the MLLM-HSGG dataset, which uses multimodal large language models (MLLMs) to enhance the scene graph generation task, aiming to provide richer structured information representation for visual understanding. Its core features include multimodal fusion, high information density, and quality improvement, driving the development of the scene graph generation field through innovative technical methods.

## Background and Motivation: Limitations of Traditional Scene Graph Generation and Opportunities of MLLMs

Scene Graph Generation (SGG) is a core task in computer vision, converting images into structured graphs (nodes as objects, edges as relationships). Traditional SGG is limited by the quality and diversity of training data, making it difficult to capture fine-grained relationships in complex scenes. In recent years, MLLMs have demonstrated strong visual-language understanding capabilities, bringing new opportunities to SGG. The MLLM-HSGG project explores the use of MLLMs to enhance dataset quality and information density.

## Project Overview: Core Features of the MLLM-HSGG Dataset

MLLM-HSGG is a dataset project focused on high-information scene graph generation. It enhances existing datasets through MLLMs to produce training data with richer relationship annotations and precise attribute descriptions. Core features:
- **Multimodal Fusion**: Combines visual features and language understanding to generate more accurate annotations
- **High Information Density**: Contains more fine-grained object relationships and attributes
- **Quality Improvement**: Uses MLLM reasoning to filter low-quality annotations and improve reliability

## Technical Methods: Innovative Strategies for Multimodal Alignment and Data Enhancement

The project adopts innovative technologies to improve results:
1. **Visual-Language Alignment**: Achieves deep alignment between images and text through MLLMs to capture complex semantic relationships
2. **Data Enhancement Strategies**: Generates diverse relationship descriptions, verifies and corrects existing annotations, and supplements missing attributes and relationships
3. **Quality Control Mechanism**: An MLLM-based quality assessment module automatically identifies and filters incorrect annotations

## Application Scenarios: Wide Applications of the MLLM-HSGG Dataset

The dataset can be applied to:
- Image Understanding: Improve the performance of visual question answering and image caption generation
- Visual Reasoning: Support complex scene understanding and logical reasoning
- Multimodal Learning: Provide high-quality data for visual-language pre-training
- Robot Navigation: Help robots understand environmental layouts and object relationships

## Technical Significance: Promoting Paradigm Shift in the Scene Graph Generation Field

The value of the project lies in exploring the application potential of MLLMs in structured visual data generation, shifting SGG from relying on visual features to visual-language joint modeling, and promoting the development of the field. It also reveals that large language models are not only used for generation tasks but also can serve as guardians and enhancers of data quality, playing a key role in scenarios where data is scarce or annotation is challenging.

## Summary and Outlook: The Future of Visual Understanding Driven by Multimodal Technology

MLLM-HSGG represents an important direction in the scene graph generation field—using MLLMs to improve data quality and model performance. With the advancement of multimodal technology, we look forward to more innovative methods emerging to drive visual understanding to a deeper level.