HIVE: Evaluating the Human Interpretability of Visual Explanations

6 December 2021

Papers citing "HIVE: Evaluating the Human Interpretability of Visual Explanations"

27 / 27 papers shown

Title
Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations Indu Panigrahi Sunnie S. Y. Kim Amna Liaqat Rohan Jinturkar Olga Russakovsky Ruth C. Fong Parastoo Abtahi FAtt HAI 57 0 0 14 Apr 2025
Show and Tell: Visually Explainable Deep Neural Nets via Spatially-Aware Concept Bottleneck Models Itay Benou Tammy Riklin-Raviv 62 0 0 27 Feb 2025
Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment Harrish Thasarathan Julian Forsyth Thomas Fel M. Kowal Konstantinos G. Derpanis 111 7 0 06 Feb 2025
Personalized Help for Optimizing Low-Skilled Users' Strategy Feng Gu Wichayaporn Wongkamjan Jordan Boyd-Graber Jonathan K. Kummerfeld Denis Peskoff Jonathan May 26 0 0 14 Nov 2024
InfoDisent: Explainability of Image Classification Models by Information Disentanglement Łukasz Struski Dawid Rymarczyk Jacek Tabor 51 0 0 16 Sep 2024
Multi-Scale Grouped Prototypes for Interpretable Semantic Segmentation Hugo Porta Emanuele Dalsasso Diego Marcos D. Tuia 93 0 0 14 Sep 2024
Inpainting the Gaps: A Novel Framework for Evaluating Explanation Methods in Vision Transformers Lokesh Badisa Sumohana S. Channappayya 40 0 0 17 Jun 2024
Graphical Perception of Saliency-based Model Explanations Yayan Zhao Mingwei Li Matthew Berger XAI FAtt 41 2 0 11 Jun 2024
Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey Rokas Gipiškis Chun-Wei Tsai Olga Kurasova 54 5 0 02 May 2024
What Sketch Explainability Really Means for Downstream Tasks Hmrishav Bandyopadhyay Pinaki Nath Chowdhury A. Bhunia Aneeshan Sain Tao Xiang Yi-Zhe Song 30 4 0 14 Mar 2024
Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image Recognition Chong Wang Yuanhong Chen Fengbei Liu Yuyuan Liu Davis J. McCarthy Helen Frazer Gustavo Carneiro 18 1 0 30 Nov 2023
Zero-shot Translation of Attention Patterns in VQA Models to Natural Language Leonard Salewski A. Sophia Koepke Hendrik P. A. Lensch Zeynep Akata 29 2 0 08 Nov 2023
FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods Robin Hesse Simone Schaub-Meyer Stefan Roth AAML 32 32 0 11 Aug 2023
Precise Benchmarking of Explainable AI Attribution Methods Rafael Brandt Daan Raatjens G. Gaydadjiev XAI 19 4 0 06 Aug 2023
In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Making Raymond Fok Daniel S. Weld 24 61 0 12 May 2023
Towards a Praxis for Intercultural Ethics in Explainable AI Chinasa T. Okolo 31 3 0 24 Apr 2023
UFO: A unified method for controlling Understandability and Faithfulness Objectives in concept-based explanations for CNNs V. V. Ramaswamy Sunnie S. Y. Kim Ruth C. Fong Olga Russakovsky 27 0 0 27 Mar 2023
Take 5: Interpretable Image Classification with a Handful of Features Thomas Norrenbrock Marco Rudolph Bodo Rosenhahn FAtt 29 7 0 23 Mar 2023
ICICLE: Interpretable Class Incremental Continual Learning Dawid Rymarczyk Joost van de Weijer Bartosz Zieliñski Bartlomiej Twardowski CLL 24 28 0 14 Mar 2023
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations Valerie Chen Q. V. Liao Jennifer Wortman Vaughan Gagan Bansal 36 103 0 18 Jan 2023
CRAFT: Concept Recursive Activation FacTorization for Explainability Thomas Fel Agustin Picard Louis Bethune Thibaut Boissin David Vigouroux Julien Colin Rémi Cadène Thomas Serre 19 102 0 17 Nov 2022
Visual correspondence-based explanations improve AI robustness and human-AI team accuracy Giang Nguyen Mohammad Reza Taesiri Anh Totti Nguyen 30 42 0 26 Jul 2022
CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations Leonard Salewski A. Sophia Koepke Hendrik P. A. Lensch Zeynep Akata LRM NAI 25 20 0 05 Apr 2022
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis Thomas Fel Mélanie Ducoffe David Vigouroux Rémi Cadène Mikael Capelle C. Nicodeme Thomas Serre AAML 20 41 0 15 Feb 2022
Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis Samuele Poppi Marcella Cornia Lorenzo Baraldi Rita Cucchiara FAtt 125 33 0 20 Apr 2021
Estimating Example Difficulty Using Variance of Gradients Chirag Agarwal Daniel D'souza Sara Hooker 208 107 0 26 Aug 2020
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 230 3,681 0 28 Feb 2017