HIVE: Evaluating the Human Interpretability of Visual Explanations
Project Page | Paper
This repo provides the code for HIVE, a human evaluation framework for interpretability methods in computer vision.
@article{kim2021hive,
author = {Sunnie S. Y. Kim and Nicole Meister and Vikram V. Ramaswamy and Ruth Fong and Olga Russakovsky},
title = {{HIVE}: Evaluating the Human Interpretability of Visual Explanations},
journal = {CoRR},
volume = {abs/2112.03184},
year = {2021}
}
Our study UIs
Distinction task
- combined_gradcam_nolabels.html
- combined_bagnet_nolabels.html
- combined_protopnet_distinction.html
- combined_prototree_distinction.html
Agreement task
- combined_protopnet_agreement.html
- combined_prototree_agreement.html
Additional studies
- combined_gradcam_labels.html
- combined_bagnet_labels.html
- combined_prototree_agreement_tree.html
Running human studies
We ran our studies through Human Intelligence Tasks (HITs) deployed on Amazon Mechanical Turk (AMT). We use simple-amt, a microframework for working with AMT. Here we describe which files correspond to which study UIs and provide brief instructions for running studies.
Brief instructions on how to run user studies on AMT
Please check out the original simple-amt repository for more information on how to run a HIT on AMT.
Launch HITs on AMT
python launch_hits.py \
--html_template=hit_templates/combined_prototree_distinction.html \
--hit_properties_file=hit_properties/properties.json \
--input_json_file=examples/input_prototree_distinction.txt \
--hit_ids_file=examples/hit_ids_prototree_distinction.txt --prod
Check HIT progress
python show_hit_progress.py \
--hit_ids_file=examples/hit_ids_prototree_distinction.txt --prod
Get results
python get_results.py \
--hit_ids_file=examples/hit_ids_prototree_distinction.txt \
--output_file=examples/results_prototree_distinction.txt \
> examples/results_prototree_distinction.txt --prod
Approve work
python approve_hits.py \
--hit_ids_file=examples/hit_ids_prototree_distinction.txt --prod