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Scene Graph Prediction with Limited Labels
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README.md

Scene Graph Prediction with Limited Labels

Overview

  • Scene graphs capture visual relationships between objects in images, e.g. <person, riding, bike>. They have emerged as useful in a number of computer vision tasks, including visual question answering and captioning.

  • However, most scene graph datasets are sparse due to annotator error!

  • This work attempts to overcome limitations of human annotators using a semi-supervised method, taking advantage of both limited labels and unlabeled data, to generate training datasets for scene graphs.

  • Approach:

    • We leverage image-agnostic features, which are cheap to extract given bounding box pairs of images.
    • With as few as n=10 labeled relationships per predicate (e.g. "walk", "ride", "eat"), we learn heuristics, shallow decision trees that can serve as noisy labelers.
    • Because these heuristics are error prone, we learn a generative model to combine and denoise the outputs of these heuristics, producing probabilistic labels for each object pair.
    • Using probabilistic labels over the unlabeled training data, we can bootstrap training for any downstream scene graph model!

Setup

Please run the following script to download the VisualGenome dataset.

./scripts/get_visualgenome.sh

To create the virtual environment with appropriate requirements:

virtualenv -p python3 .env
source .env/bin/activate
pip install -r requirements.txt

Demo

All instructions for this demonstration are included in main.ipynb.

Paper

You can find the full paper corresponding to this work, presented at ICCV 2019, at https://arxiv.org/abs/1904.11622.

Bibtex

Please refer to the following citation if you are building on this work:

@inproceedings{Chen_2019_ICCV,
    author = {Chen, Vincent S. and Varma, Paroma and Krishna, Ranjay and Bernstein, Michael and Re, Christopher and Fei-Fei, Li},
    title = {Scene Graph Prediction With Limited Labels},
    booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
    month = {October},
    year = {2019}
}

Contributions

Feel free to open an issue or send an email if you have any questions!

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