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Explaining with Counter Visual Attributes and Examples

Authors:

Arnold SmeuldersAuthors Info & Claims

ICMR '20: Proceedings of the 2020 International Conference on Multimedia Retrieval

Pages 35 - 43

https://doi.org/10.1145/3372278.3390672

Published: 08 June 2020 Publication History

Abstract

In this paper, we aim to explain the decisions of neural networks by utilizing multimodal information. That is counter-intuitive attributes and counter visual examples which appear when perturbed samples are introduced. Different from previous work on interpreting decisions using saliency maps, text, or visual patches we propose to use attributes and counter-attributes, and examples and counter-examples as part of the visual explanations. When humans explain visual decisions they tend to do so by providing attributes and examples. Hence, inspired by the way of human explanations in this paper we provide attribute-based and example-based explanations. Moreover, humans also tend to explain their visual decisions by adding counter-attributes and counter-examples to explain what isnot seen. We introduce directed perturbations in the examples to observe which attribute values change when classifying the examples into the counter classes. This delivers intuitive counter-attributes and counter-examples. Our experiments with both coarse and fine-grained datasets show that attributes provide discriminating and human-understandable intuitive and counter-intuitive explanations.

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Cited By

Rodis NSardianos CRadoglou-Grammatikis PSarigiannidis PVarlamis IPapadopoulos G(2024)Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research DirectionsIEEE Access10.1109/ACCESS.2024.346706212(159794-159820)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3467062
Antamis TDrosou AVafeiadis TNizamis AIoannidis DTzovaras D(2024)Interpretability of deep neural networksNeurocomputing10.1016/j.neucom.2024.128204601:COnline publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.128204
Buric FViknander SFu XLemke OCarmona OZrimec JSzyrwiel LMülleder MRalser MZelezniak A(2024)Amino acid sequence encodes protein abundance shaped by protein stability at reduced synthesis costProtein Science10.1002/pro.523934:1Online publication date: 12-Dec-2024
https://doi.org/10.1002/pro.5239
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Index Terms

Explaining with Counter Visual Attributes and Examples
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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Published In

cover image ACM Conferences

ICMR '20: Proceedings of the 2020 International Conference on Multimedia Retrieval

June 2020

605 pages

ISBN:9781450370875

DOI:10.1145/3372278

General Chairs:
Cathal Gurrin
Dublin City University, Ireland
,
Björn Þór Jónsson
IT University of Copenhagen, Denmark
,
Noriko Kando
National Institute of Informatics, Tokyo
,
Program Chairs:
Klaus Schoeffmann
Klagenfurt University, Austria
,
Phoebe Chen
La Trobe University, Australia
,
Noel E. O'Connor
Dublin City University, Ireland

Copyright © 2020 ACM.

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

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Published: 08 June 2020

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ICMR '20

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ICMR '20: International Conference on Multimedia Retrieval

June 8 - 11, 2020

Dublin, Ireland

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Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Rodis NSardianos CRadoglou-Grammatikis PSarigiannidis PVarlamis IPapadopoulos G(2024)Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research DirectionsIEEE Access10.1109/ACCESS.2024.346706212(159794-159820)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3467062
Antamis TDrosou AVafeiadis TNizamis AIoannidis DTzovaras D(2024)Interpretability of deep neural networksNeurocomputing10.1016/j.neucom.2024.128204601:COnline publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.128204
Buric FViknander SFu XLemke OCarmona OZrimec JSzyrwiel LMülleder MRalser MZelezniak A(2024)Amino acid sequence encodes protein abundance shaped by protein stability at reduced synthesis costProtein Science10.1002/pro.523934:1Online publication date: 12-Dec-2024
https://doi.org/10.1002/pro.5239
Park HLee SHoover BWright AShaikh ODuggal RDas NLi KHoffman JChau DFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and DiscoveriesProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614819(2044-2054)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614819
Chen RLi JZhang HSheng CLiu LCao X(2023)Sim2Word: Explaining Similarity with Representative Attribute Words via Counterfactual ExplanationsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/356303919:6(1-22)Online publication date: 12-Jul-2023
https://dl.acm.org/doi/10.1145/3563039
Liu SLi JZhang HXu LCao X(2023)Prediction With Visual Evidence: Sketch Classification Explanation via Stroke-Level AttributionsIEEE Transactions on Image Processing10.1109/TIP.2023.329740432(4393-4406)Online publication date: 2023
https://doi.org/10.1109/TIP.2023.3297404
Gulshad SLong Tvan Noord N(2023)Hierarchical Explanations for Video Action Recognition2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW59228.2023.00379(3703-3708)Online publication date: Jun-2023
https://doi.org/10.1109/CVPRW59228.2023.00379
Joshi GWalambe RKotecha K(2021)A Review on Explainability in Multimodal Deep Neural NetsIEEE Access10.1109/ACCESS.2021.30702129(59800-59821)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3070212
Gulshad SSmeulders A(2021)Counterfactual attribute-based visual explanations for classificationInternational Journal of Multimedia Information Retrieval10.1007/s13735-021-00208-310:2(127-140)Online publication date: 18-Apr-2021
https://doi.org/10.1007/s13735-021-00208-3

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