Machine-in-the-Loop Rewriting for Creative Image Captioning

Last update: Jul 24, 2022

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Overview

Machine-in-the-Loop Rewriting for Creative Image Captioning

Data

Annotated sources of data used in the paper:

Data Source	URL
Mohammed et al.	Link
Gordon et al.	Link
Bostan et al.	Link
Niculae et al.	Link
Steen et al.	Link

TODO: Individual data cleaning scripts

Model Training

Follow the README in the model_training directory to train a Fairseq BART model. Reach out for our trained model.

Interface

Code to run the UI we used for interactive experiments. This UI hosts a server and needs you to have a backend GPU to run model inference during interaction. The code saves each interaction with a unique ID which we use to match to our crowdworkers for experimental analysis.

TODO: Data Processing Scripts to filter results

Owner

Vishakh P

GitHub Repository

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Machine-in-the-Loop Rewriting for Creative Image Captioning

Related tags

Overview

Machine-in-the-Loop Rewriting for Creative Image Captioning

Data

TODO: Individual data cleaning scripts

Model Training

Interface

TODO: Data Processing Scripts to filter results

Owner

Vishakh P

Deployment of PyTorch chatbot with Flask

Official Tensorflow implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation (ICLR 2020)

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This script runs neural style transfer against the provided content image.