Structural Reasoning for Image-based Social Relation Recognition

Note

End-to-end implementation of the Social Graph Network (SGN) and all the experiments described in the Structural Reasoning for Image-based Social Relation Recognition paper. If you find the contents of this repository useful in your research, please consider citing us:

@article{Sousa2023StructuralRF,
  title = {Structural reasoning for image-based social relation recognition},
  journal = {Computer Vision and Image Understanding},
  volume = {235},
  pages = {103785},
  year = {2023},
  issn = {1077-3142},
  doi = {https://doi.org/10.1016/j.cviu.2023.103785},
  url = {https://www.sciencedirect.com/science/article/pii/S1077314223001650},
  author = {Eduardo {V. Sousa} and Douglas {G. Macharet}},
  keywords = {Social relation recognition, Scene understanding, Group interaction, Image classification}
}

Overview

The image-based relation recognition task consists of identifying the social connections between each pair of individuals in a given picture. In this work, we consider multiple information scopes from the input image and their correlations to achieve the high-level reasoning required to identify social relations, as illustrated in the following image:

To achieve this, we developed a model that combines multiple convolutional backbones specializing in each scope. Their features are used to generate an unique graph representation which is employed to perform relation reasoning. This is done by our proposed deep graph model, which is composed of three distinct spatial graph convolution methods designed to exploit the unique representation. An overview of the framework is shown in the following figure:

Installation

Create a new environment and activate it, then clone the repository and install the requirements using the following commands:

git clone https://github.com/eduardoatr/Structural_Reasoning_SRR.git
cd Structural_Reasoning_SRR
pip install -r requirements.txt

Datasets

Download the benchmark datasets:

Additionally, download the relationship annotations for the PIPA dataset.

Important

Unfortunately, the PIPA dataset has disappeared from the internet, but you can download an intact copy of the source files I have made available on Google Drive. The dataset was published by Zhang, Ning et al. in the "Beyond frontal faces: Improving Person Recognition using multiple cues" paper, under the Creative Commons 2.0 license. More information about the dataset is presented into the annotations folder.

Pre-trained Models

The pre-trained models employed for attribute features extraction can be obtained from:

Pre-processing

To run the provided model, first, it is necessary to execute some pre-processing steps over the original data, shifting the previous pair-wise paradigm to an image-based approach. The following instructions provide information on how to set up the source data for this process.

Directories structure

Unpack the downloaded data, generating the following directories structure:

.
└── datasets
    ├── PIPA
    |   ├── annotations
    |   ├── images
    |   |   ├── body
    |   |   ├── face
    |   |   └── full
    |   ├── objects
    |   └── splits
    |       ├── consistency_annotator
    |       └── consistency_relation
    └── PISC
        ├── annotations
        ├── images
        |   └── full
        ├── objects
        └── splits
            ├── domain
            └── relationship

Allocate each type of image provided by the original data to their respective folders (body, face, and full). The train/test protocols should be assigned to the splits folder, and metadata files should stay in the annotations folder.

Processing data

Fill the correct paths and configurations in the processing_datasets.sh script, then run it to generate the final metadata files merging the original annotations to consider an image-based approach.

Feature extraction

To use the given feature generation scripts, organize the downloaded extraction models according to the following structure:

.
└── models
    └── Caffe
        ├── models
        └── protocols

Allocate the Caffe protocols and the models to their respective folders, then execute the extracting_atf.sh script after filling paths and other configuration information.

Training and Testing

Fill in the necessary parameters for the models and use the training.sh and testing.sh scripts to train a model and run it on inference mode.

verlab/Structural_Reasoning_SRR