HIVE: Evaluating the Human Interpretability of Visual Explanations

Related tags

Deep LearningHIVE
Overview

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
Owner
Princeton Visual AI Lab
Princeton Visual AI Lab
A PyTorch implementation of "TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?"

TokenLearner: What Can 8 Learned Tokens Do for Images and Videos? Source: Improving Vision Transformer Efficiency and Accuracy by Learning to Tokenize

Caiyong Wang 14 Sep 20, 2022
Rasterize with the least efforts for researchers.

utils3d Rasterize and do image-based 3D transforms with the least efforts for researchers. Based on numpy and OpenGL. It could be helpful when you wan

Ruicheng Wang 8 Dec 15, 2022
Tackling data scarcity in Speech Translation using zero-shot multilingual Machine Translation techniques

Tackling data scarcity in Speech Translation using zero-shot multilingual Machine Translation techniques This repository is derived from the NMTGMinor

Tu Anh Dinh 1 Sep 07, 2022
RMTD: Robust Moving Target Defence Against False Data Injection Attacks in Power Grids

RMTD: Robust Moving Target Defence Against False Data Injection Attacks in Power Grids Real-time detection performance. This repo contains the code an

0 Nov 10, 2021
Uni-Fold: Training your own deep protein-folding models

Uni-Fold: Training your own deep protein-folding models. This package provides an implementation of a trainable, Transformer-based deep protein foldin

DP Technology 187 Jan 04, 2023
Raster Vision is an open source Python framework for building computer vision models on satellite, aerial, and other large imagery sets

Raster Vision is an open source Python framework for building computer vision models on satellite, aerial, and other large imagery sets (including obl

Azavea 1.7k Dec 22, 2022
Python KNN model: Predicting a probability of getting a work visa. Tableau: Non-immigrant visas over the years.

The value of international students to the United States. Probability of getting a non-immigrant visa. Project timeline: Jan 2021 - April 2021 Project

Zinaida Dvoskina 2 Nov 21, 2021
PyTorch code for MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning

MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning PyTorch code for our ACL 2020 paper "MART: Memory-Augmented Recur

Jie Lei 雷杰 151 Jan 06, 2023
Companion repo of the UCC 2021 paper "Predictive Auto-scaling with OpenStack Monasca"

Predictive Auto-scaling with OpenStack Monasca Giacomo Lanciano*, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella 2021 IEEE/ACM 14t

Giacomo Lanciano 0 Dec 07, 2022
Fast methods to work with hydro- and topography data in pure Python.

PyFlwDir Intro PyFlwDir contains a series of methods to work with gridded DEM and flow direction datasets, which are key to many workflows in many ear

Deltares 27 Dec 07, 2022
Boosted CVaR Classification (NeurIPS 2021)

Boosted CVaR Classification Runtian Zhai, Chen Dan, Arun Sai Suggala, Zico Kolter, Pradeep Ravikumar NeurIPS 2021 Table of Contents Quick Start Train

Runtian Zhai 4 Feb 15, 2022
PyTorch implementation of "Conformer: Convolution-augmented Transformer for Speech Recognition" (INTERSPEECH 2020)

PyTorch implementation of Conformer: Convolution-augmented Transformer for Speech Recognition. Transformer models are good at capturing content-based

Soohwan Kim 565 Jan 04, 2023
PerfFuzz: Automatically Generate Pathological Inputs for C/C++ programs

PerfFuzz Performance problems in software can arise unexpectedly when programs are provided with inputs that exhibit pathological behavior. But how ca

Caroline Lemieux 125 Nov 18, 2022
Hooks for VCOCO

Verbs in COCO (V-COCO) Dataset This repository hosts the Verbs in COCO (V-COCO) dataset and associated code to evaluate models for the Visual Semantic

Saurabh Gupta 131 Nov 24, 2022
A repository for benchmarking neural vocoders by their quality and speed.

License The majority of VocBench is licensed under CC-BY-NC, however portions of the project are available under separate license terms: Wavenet, Para

Meta Research 177 Dec 12, 2022
Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression.

Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression. Not an official Google product. Me

Google Research 27 Dec 12, 2022
Simple-System-Convert--C--F - Simple System Convert With Python

Simple-System-Convert--C--F REQUIREMENTS Python version : 3 HOW TO USE Run the c

Jonathan Santos 2 Feb 16, 2022
Code for the paper Task Agnostic Morphology Evolution.

Task-Agnostic Morphology Optimization This repository contains code for the paper Task-Agnostic Morphology Evolution by Donald (Joey) Hejna, Pieter Ab

Joey Hejna 18 Aug 04, 2022
This is a Machine Learning Based Hand Detector Project, It Uses Machine Learning Models and Modules Like Mediapipe, Developed By Google!

Machine Learning Hand Detector This is a Machine Learning Based Hand Detector Project, It Uses Machine Learning Models and Modules Like Mediapipe, Dev

Popstar Idhant 3 Feb 25, 2022
An implementation of the Contrast Predictive Coding (CPC) method to train audio features in an unsupervised fashion.

CPC_audio This code implements the Contrast Predictive Coding algorithm on audio data, as described in the paper Unsupervised Pretraining Transfers we

Meta Research 283 Dec 30, 2022