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LLaVA-SpaceSGG

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Overview

LLaVA-SpaceSGG is a multimodal large language model (MLLM) designed to tackle the challenges of Scene Graph Generation (SGG) by improving spatial relation modeling and enabling open-vocabulary generalization. SGG converts visual scenes into structured graph representations, providing deeper scene understanding for complex vision tasks.

Key Features

• Enhanced Spatial Relation Modeling: Incorporates object locations, relations, and depth information for better spatial reasoning.
• Open-Vocabulary Generalization: Excels in generating structured scene graphs in open-vocabulary contexts.
• Custom Dataset: SpaceSGG: A novel instruction-tuning dataset that includes spatial descriptions, question-answering (QA), and conversations.
• Two-Stage Training Paradigm: Improves model transferability to SGG tasks by leveraging MLLMs' native capabilities.

Achievements

• Performance: LLaVA-SpaceSGG outperforms existing methods with a 4.5% improvement in recall and a 1.4% increase in mean recall.
• Dataset: SpaceSGG is constructed using a pipeline that integrates object locations, spatial relations, and depth information from public datasets and open-source models.

Installation

Clone the repository and set up the environment:

git clone https://github.com/Endlinc/LLaVA-SpaceSGG.git
cd LLaVA-SpaceSGG
pip install -r requirements.txt

Data Preparation

Stage 1: Generate Point Clouds and Layered Objects

The scene graph description generation process in Stage 1 is built upon the All-Seeing v2 project. Please refer to their repository for detailed instructions and implementation.

1. Generate Point Cloud from RGB and Depth Image:

   python d2p.py --dataset-path dataset/coco --scale-factor 5000 --world-coordinates
   

2. Cluster Objects by Depth into Layers:

   python layers_aggregation.py \
       --input-file asv2_level.json \
       --depth-dir ./depth-output \
       --mask-dir ./mask-output \
       --output-file processed_annotations.json \
       --dataset-base /home/ming/Datasets/all-seeing-v2/materials/ \
       --data-prefix ../data/
   

3. Generate Multiview Layered Objects:

   python multiview_layers.py \
       --input-file asv2_level.json \
       --point-cloud-dir ./point_clouds \
       --mask-dir ./mask-output \
       --output-file processed_annotations.json \
       --dataset-base /home/ming/Datasets/all-seeing-v2/materials/ \
       --data-prefix ../data/
   

Stage 2: Generate Training Data Formats

1. Generate Layered Descriptions:

   python llm_based_query.py \
       --anno-file annotations.json \
       --prompt-function create_layer_prompt \
       --output-file layer_description.json
   

2. Generate Question-Answering (QA) Data:

   python llm_based_query.py \
       --anno-file annotations.json \
       --prompt-function create_between_prompt \
       --output-file between_qa.json
   

3. Generate Conversation Data:

   python llm_based_query.py \
       --anno-file annotations.json \
       --prompt-function create_rotation_prompt \
       --output-file rotation_prompts.json
   

Usage

After preparing the dataset, train the LLaVA-SpaceSGG model using the scripts provide in project LLaVA and The All-Seeing Project V2

Citation

If you use LLaVA-SpaceSGG or SpaceSGG dataset in your research, please cite our work:

@inproceedings{llava_spacesgg2025,
  title={LLaVA-SpaceSGG: Visual Instruct Tuning for Open-vocabulary Scene Graph Generation with Enhanced Spatial Relations},
  author={Your Name and Co-authors},
  booktitle={Proceedings of WACV 2025},
  year={2025}
}

License

This project is licensed under the Apache License.

Contact

For questions or feedback, please contact parasolohalo@gmail.com.

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