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
Using LSTM to detect spoofing attacks in an Air-Ground network

Using LSTM to detect spoofing attacks in an Air-Ground network Specifications IDE: Spider Packages: Tensorflow 2.1.0 Keras NumPy Scikit-learn Matplotl

Tiep M. H. 1 Nov 20, 2021
FairMOT for Multi-Class MOT using YOLOX as Detector

FairMOT-X Project Overview FairMOT-X is a multi-class multi object tracker, which has been tailored for training on the BDD100K MOT Dataset. It makes

Jonathan Tan 33 Dec 28, 2022
Multi-task Learning of Order-Consistent Causal Graphs (NeuRIPs 2021)

Multi-task Learning of Order-Consistent Causal Graphs (NeuRIPs 2021) Authors: Xinshi Chen, Haoran Sun, Caleb Ellington, Eric Xing, Le Song Link to pap

Xinshi Chen 2 Dec 20, 2021
A repository for interferometer controller code.

dses-interferometer-controller A repository for interferometer controller code, hardware, and simulations. See dses.science for more information on th

Eli Reed 1 Jan 17, 2022
CDGAN: Cyclic Discriminative Generative Adversarial Networks for Image-to-Image Transformation

CDGAN CDGAN: Cyclic Discriminative Generative Adversarial Networks for Image-to-Image Transformation CDGAN Implementation in PyTorch This is the imple

Kancharagunta Kishan Babu 6 Apr 19, 2022
CSD: Consistency-based Semi-supervised learning for object Detection

CSD: Consistency-based Semi-supervised learning for object Detection (NeurIPS 2019) By Jisoo Jeong, Seungeui Lee, Jee-soo Kim, Nojun Kwak Installation

80 Dec 15, 2022
Tilted Empirical Risk Minimization (ICLR '21)

Tilted Empirical Risk Minimization This repository contains the implementation for the paper Tilted Empirical Risk Minimization ICLR 2021 Empirical ri

Tian Li 40 Nov 28, 2022
Learning Neural Painters Fast! using PyTorch and Fast.ai

The Joy of Neural Painting Learning Neural Painters Fast! using PyTorch and Fast.ai Blogpost with more details: The Joy of Neural Painting The impleme

Libre AI 72 Nov 10, 2022
Machine Learning University: Accelerated Computer Vision Class

Machine Learning University: Accelerated Computer Vision Class This repository contains slides, notebooks, and datasets for the Machine Learning Unive

AWS Samples 1.3k Dec 28, 2022
Face Mask Detection is a project to determine whether someone is wearing mask or not, using deep neural network.

face-mask-detection Face Mask Detection is a project to determine whether someone is wearing mask or not, using deep neural network. It contains 3 scr

amirsalar 13 Jan 18, 2022
《Geo Word Clouds》paper implementation

《Geo Word Clouds》paper implementation

Russellwzr 2 Jan 28, 2022
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation (CVPR 2021)

Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation Input Image Initial CAM Successive Maps with adversar

Jungbeom Lee 110 Dec 07, 2022
SegNet-Basic with Keras

SegNet-Basic: What is Segnet? Deep Convolutional Encoder-Decoder Architecture for Semantic Pixel-wise Image Segmentation Segnet = (Encoder + Decoder)

Yad Konrad 81 Jun 30, 2022
Easy and Efficient Object Detector

EOD Easy and Efficient Object Detector EOD (Easy and Efficient Object Detection) is a general object detection model production framework. It aim on p

381 Jan 01, 2023
The aim of the game, as in the original one, is to find a specific image from a group of different images of a person's face

GUESS WHO Main Links: [Github] [App] Related Links: [CLIP] [Celeba] The aim of the game, as in the original one, is to find a specific image from a gr

Arnau - DIMAI 3 Jan 04, 2022
Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language (NeurIPS 2021)

VRDP (NeurIPS 2021) Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language Mingyu Ding, Zhenfang Chen, Tao Du, Pin

Mingyu Ding 36 Sep 20, 2022
Source Code for AAAI 2022 paper "Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching"

Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching This repository is an official implementation of

HKUST-KnowComp 13 Sep 08, 2022
Code release for "Making a Bird AI Expert Work for You and Me".

Making-a-Bird-AI-Expert-Work-for-You-and-Me Code release for "Making a Bird AI Expert Work for You and Me". arxiv (Coming soon...) Changelog 2021/12/6

PRIS-CV: Computer Vision Group 11 Dec 11, 2022
Pytorch implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Detection"

M-LSD: Towards Light-weight and Real-time Line Segment Detection Pytorch implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Det

123 Jan 04, 2023
Towards Implicit Text-Guided 3D Shape Generation (CVPR2022)

Towards Implicit Text-Guided 3D Shape Generation Towards Implicit Text-Guided 3D Shape Generation (CVPR2022) Code for the paper [Towards Implicit Text

55 Dec 16, 2022