HIVE: Evaluating the Human Interpretability of Visual Explanations

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This repo provides the code for HIVE, a human evaluation framework for computer vision interpretability methods.

@inproceedings{Kim2022HIVE,
  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},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year = {2022}
}

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