• This is an implementation of the paper: Learning a Visually Grounded Memory Assistant
• This repository contains the data, the data analysis and the results for TaskA presented in the paper
• For information on how the dataset was collected or to get access to the AMT set up please contact the authors.
• This repository focuses Visual Memory Assistant tasks on the presented dataset

Contains human participants participating in the task Memory Question Answering with Navigation (MemQA).

Please refer the video to understand the task further.

The task was encapsulated in the 3D simulated indoor environments from the Matterport3D dataset and was run on Amazon Mechanical Turk.

We present 2 tasks on top of the dataset which are useful to realization of an AR assistant -- to predict when humans request assistance on visual-memory tasks. We present results for task A.

Take in the fly-through and a single question. Predict: correctness, navigation behavior, assistance request behavior.

Take in the fly-through, all four questions and the sequence of frames during the answering phase. At each time step of the answering phase predict behavior: navigation, request for assistance, answer selection or nothing

If you find something wrong or have a question, feel free to open an issue. If you would like to contribute, please install pre-commit before making commits in a pull request:

python -m pip install pre-commit
pre-commit install

If you use The Visually Grounded Memory Assistant Dataset in your research, please cite the following paper:

@inproceedings{hahn2020visualassistant,
  title={Learning a Visually Grounded Memory Assistan},
  author={Hahn, Meera and Carlberg, Kevin and Desai, Ruta and Batra, Dhruv and Hillis, James},
  booktitle={Arxiv},
  year={2020}
}