EMISSOR
EMISSOR stands for Episodic Memories and Interpretations with Situated Scenario-based Ontological References.
EMISSOR is a platform to represent and annotate multimodal interaction in combination with an episodic memory for referential grounding and cumulative knowledge growth from interactions.
The platform stores streams of multiple modalities as parallel signals. Each signal can be segmented independently and annotated with interpretations. However, these annotations do not stand on their own, but they are eventually mapped to explicit identities, relations, and properties in an episodic Knowledge Graph (eKG) for capturing instances of situations. Our model grounds signal segments to formal instance representations and it grounds different modalities (e.g. vision and references in dialogues) across each other through these representations. EMISSOR captures natural conversations in situated contexts in which actions and utterances can be responses to each other in situations but can also happen independently. We achieve this through a flexible and robust data architecture with separate independent storage of data in different modalities that can be aligned through so-called temporal and spatial containers. Through this representation, we can record and annotate experiments, share data, evaluate system behavior and their performance for preset goals but also model the accumulation of knowledge and interpretations in the eKG as a result of these episodic experiences.
Although EMISSOR can be connected to any kind of eKG to model situations, this release includes an episodic memory that supports reasoning over conflicting information and uncertainties that may result from multimodal experiences. Our model allows for different interpretations across modalities, sources and experiences. Finally note that EMISSOR can also be used without any eKG to represent and annotate multimodal interaction data.
Data Representation
This repository provides the emissor.representation Python package with data classes for
the representation of multi-modal interaction. A detailed description of the representation model can be found in the
README of this package. For usage outside of this repository a distribution of the
package can be built from the setup.py by executing
> python setup.py sdist
Annotation Tool
In addition to the emissor.representation package, this repo provides a tool that allows you to load multi-modal interaction
data with annotations and to manually edit the data by grounding it to the
temporal and spatial containers ads well as by adding any annotations. For a
detailed description see the README of the annotation
tool.
Docker image
The annotation tool is also available as Docker image numblr/emissorui that can be run using:
docker run --rm -v </host/path/to/data>:/emissorui/data/ -p 5000:5000 numblr/emissorui
This will run the container with the default port 5000 and mount your local data directory to /emissorui/data in the container. To use a different port use the following command:
docker run --rm -v </host/path/to/data>:/emissorui/data/ -p 8080:8080 -e EMISSOR_PORT=8080 numblr/emissorui
Note that the host port must be mapped to the same port in the container to avoid errors related to cross-site scripting restrictions in the web browser.
Once the Docker image is running, you can connect to the UI in the browser under http://localhost:5000 (or the
configured port).
Example data
Example data can be found in example_data/ directory. Some of them are annotated by human and some are by machine. You can visualize them with the annotation tool. We highly recommend this, since it gives you how the modalities are referenced / grounded with each other.
How to create EMISSOR annotations from existing datasets
This repo collects multimodal datasets, process them, and annotate them in the EMISSOR annotation format.
This is done by Taewoon Kim. Ask him if you have any questions.
Contributing
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Citation
Please cite the following paper if you use EMISSOR in your research:
{inproceedings@{emissor:2021, title = {EMISSOR: A platform for capturing multimodal interactions as Episodic Memories and Interpretations with Situated Scenario-based Ontological References}, author = {Selene Baez Santamaria and Thomas Baier and Taewoon Kim and Lea Krause and Jaap Kruijt and Piek Vossen}, url={https://mmsr-workshop.github.io/programme}, booktitle = {Processings of the MMSR workshop "Beyond Language: Multimodal Semantic Representations", IWSC2021}, year = {2021} }
License
Distributed under the MIT License. See LICENSE for more information.
