Multimodal Dataset Generation and Reasoning: Workflow Practice for Building Vision-Language Reasoning Data

The title of this project is "Multimodal Dataset Generation and Reasoning: Workflow Practice for Building Vision-Language Reasoning Data". Its core is to systematically organize dataset construction methods for generative reasoning in multimodal large language models, providing a complete workflow from data generation and automatic annotation to quality assessment, with a special focus on spatial and visual reasoning tasks. The project aims to help researchers translate methodological insights from literature into reproducible and scalable data pipelines.

Multimodal large language models (such as GPT-4V, Gemini, LLaVA, Qwen-VL) demonstrate strong visual reasoning capabilities, but the support of high-quality multimodal datasets is crucial. Multimodal data construction faces three major challenges: high collection costs, complex quality control (need to ensure semantic alignment between images and text), and high requirements for task diversity. Currently, there are two main issues: large-scale crawled datasets (e.g., LAION-5B, COYO-700M) have uneven quality and lack reasoning task design; manually annotated datasets (e.g., VQAv2, GQA) are reliable in quality but limited in scale. Building high-quality, scalable multimodal datasets optimized for reasoning tasks has become a key bottleneck in promoting the development of multimodal AI.

The project does not propose a new dataset; instead, it systematically organizes best practices from academia and implements them engineeringly. The design principles include: modular architecture (independent generation, annotation, filtering, and evaluation modules that can be flexibly combined); model agnosticism (adapting to different model architectures and training paradigms); educational orientation (code and documentation emphasize clarity and readability); lightweight implementation (avoiding over-engineering to lower the threshold for use).

The core components include: 1. Data directories: raw_assets (original visual materials), generated_qa (automatically generated question-answer pairs), curated_datasets (high-quality subsets), splits (training/validation/test splits); 2. Prompt library: VQA generation prompts, spatial relationship prompts, quality check prompts; 3. Script tools: automatic generation scripts, automatic annotation scripts, data filtering scripts, merging and splitting scripts; 4. Analysis notebooks: e.g., COCO spatial VQA dataset construction example; 5. Evaluation modules: rationality check, simple baseline evaluation (using CLIP/BLIP to verify data quality).

The project applies to three types of scenarios: 1. Building datasets from scratch: refer to the complete pipeline and use LLM to generate low-cost synthetic data; 2. Expanding existing datasets: use partial components (e.g., spatial prompts, automatic annotation scripts) to enhance reasoning annotations of existing data; 3. Prototype validation and rapid iteration: quickly generate small-scale targeted datasets to evaluate specific reasoning capabilities of models (e.g., spatial reasoning).

Technical innovations include: 1. Programmatic spatial fact generation: derive spatial relationships based on image bounding boxes, then convert them into natural language question-answer pairs via LLM; 2. Iterative prompt optimization: analyze the quality of generated data and adjust prompt templates to improve output quality; 3. Quality-diversity trade-off: use strategies such as stratified sampling, embedding diversity measurement, and human-machine collaborative review to balance data quality and diversity.

Current limitations: 1. Scale limitation (focuses on clarity rather than large-scale processing capability); 2. Language coverage (mainly focuses on English data); 3. Domain specificity (examples focus on general tasks and need to be adapted to specific domains such as medical imaging); 4. Ethical considerations (lack of tool support for risks like harmful content, bias, and privacy). Improvement directions need to address the above issues targetedly.

High-quality data is the cornerstone of AI progress, and this project provides a practical toolbox for the research community. Both researchers who want to understand multimodal data construction methods and developers who want to quickly create targeted datasets can benefit from it. The open-source nature of the project supports continuous community improvement, promotes the development of multimodal AI, and aligns with the data-centric AI trend.