VISLIX: An XAI Framework for Validating Vision Models with Slice Discovery and Analysis
Real‐world machine learning models require rigorous evaluation before deployment, especially in safety‐critical domains like autonomous driving and surveillance. The evaluation of machine learning models often focuses on data slices, which are subsets of the data that share a set of characteristics....
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| Vydáno v: | Computer graphics forum Ročník 44; číslo 3 |
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Oxford
Blackwell Publishing Ltd
01.06.2025
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | Real‐world machine learning models require rigorous evaluation before deployment, especially in safety‐critical domains like autonomous driving and surveillance. The evaluation of machine learning models often focuses on data slices, which are subsets of the data that share a set of characteristics. Data slice finding automatically identifies conditions or data subgroups where models underperform, aiding developers in mitigating performance issues. Despite its popularity and effectiveness, data slicing for vision model validation faces several challenges. First, data slicing often needs additional image metadata or visual concepts, and falls short in certain computer vision tasks, such as object detection. Second, understanding data slices is a labor‐intensive and mentally demanding process that heavily relies on the expert's domain knowledge. Third, data slicing lacks a human‐in‐the‐loop solution that allows experts to form hypothesis and test them interactively. To overcome these limitations and better support the machine learning operations lifecycle, we introduce VISLIX, a novel visual analytics framework that employs state‐of‐the‐art foundation models to help domain experts analyze slices in computer vision models. Our approach does not require image metadata or visual concepts, automatically generates natural language insights, and allows users to test data slice hypothesis interactively. We evaluate VISLIX with an expert study and three use cases, that demonstrate the effectiveness of our tool in providing comprehensive insights for validating object detection models. |
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| AbstractList | Real‐world machine learning models require rigorous evaluation before deployment, especially in safety‐critical domains like autonomous driving and surveillance. The evaluation of machine learning models often focuses on data slices, which are subsets of the data that share a set of characteristics. Data slice finding automatically identifies conditions or data subgroups where models underperform, aiding developers in mitigating performance issues. Despite its popularity and effectiveness, data slicing for vision model validation faces several challenges. First, data slicing often needs additional image metadata or visual concepts, and falls short in certain computer vision tasks, such as object detection. Second, understanding data slices is a labor‐intensive and mentally demanding process that heavily relies on the expert's domain knowledge. Third, data slicing lacks a human‐in‐the‐loop solution that allows experts to form hypothesis and test them interactively. To overcome these limitations and better support the machine learning operations lifecycle, we introduce VISLIX, a novel visual analytics framework that employs state‐of‐the‐art foundation models to help domain experts analyze slices in computer vision models. Our approach does not require image metadata or visual concepts, automatically generates natural language insights, and allows users to test data slice hypothesis interactively. We evaluate VISLIX with an expert study and three use cases, that demonstrate the effectiveness of our tool in providing comprehensive insights for validating object detection models. |
| Author | Ono, Jorge Piazentin Wang, Bei Yan, Xinyuan Xuan, Xiwei Guo, Jiajing Kumar, Shekar Arvind Mohanty, Vikram Gou, Liang Ren, Liu |
| Author_xml | – sequence: 1 givenname: Xinyuan orcidid: 0000-0003-3396-1310 surname: Yan fullname: Yan, Xinyuan organization: Scientific Computing and Imaging Institute, University of Utah – sequence: 2 givenname: Xiwei orcidid: 0000-0002-0828-8761 surname: Xuan fullname: Xuan, Xiwei organization: University of California – sequence: 3 givenname: Jorge Piazentin orcidid: 0000-0002-2424-0186 surname: Ono fullname: Ono, Jorge Piazentin organization: Bosch Research North America and Bosch Center for Artificial Intelligence (BCAI) – sequence: 4 givenname: Jiajing orcidid: 0000-0003-0511-136X surname: Guo fullname: Guo, Jiajing organization: Bosch Research North America and Bosch Center for Artificial Intelligence (BCAI) – sequence: 5 givenname: Vikram orcidid: 0000-0001-6296-3134 surname: Mohanty fullname: Mohanty, Vikram organization: Bosch Research North America and Bosch Center for Artificial Intelligence (BCAI) – sequence: 6 givenname: Shekar Arvind orcidid: 0000-0002-5853-5310 surname: Kumar fullname: Kumar, Shekar Arvind organization: Robert Bosch GmbH – sequence: 7 givenname: Liang orcidid: 0009-0006-9138-3351 surname: Gou fullname: Gou, Liang organization: Splunk Technology – sequence: 8 givenname: Bei orcidid: 0000-0002-9240-0700 surname: Wang fullname: Wang, Bei organization: Scientific Computing and Imaging Institute, University of Utah – sequence: 9 givenname: Liu orcidid: 0009-0002-1813-8844 surname: Ren fullname: Ren, Liu organization: Bosch Research North America and Bosch Center for Artificial Intelligence (BCAI) |
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| Cites_doi | 10.1109/JPROC.2023.3238524 10.1109/ICCV.2015.135 10.1109/CVPR42600.2020.01164 10.1145/3531146.3533240 10.1145/3640543.3645163 10.32614/CRAN.package.uwot 10.1007/s11263-019-01247-4 10.1109/TMM.2016.2642789 10.1109/ICDE.2019.00139 10.1145/3035918.3035928 10.1145/3479569 10.3390/app13084956 10.1109/TVCG.2020.3030350 10.1145/3544548.3581373 10.1109/ICCV.2015.169 10.1145/335191.335372 10.1145/3544548.3581268 10.18653/v1/2020.findings-emnlp.253 10.1145/3448016.3457323 10.1145/3448016.3457284 10.1007/978-3-642-37456-2_14 10.1109/ICPR48806.2021.9413131 10.1016/0377-0427(87)90125-7 10.1038/s41586-019-1138-y 10.1109/TVCG.2025.3546644 10.1609/hcomp.v11i1.27548 10.18653/v1/2021.findings-acl.336 10.1145/3544548.3581555 10.1109/CVPR52733.2024.02484 10.1016/j.isprsjprs.2022.12.021 10.18653/v1/2022.findings-naacl.31 |
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| SubjectTerms | CCS Concepts Computer vision Computer vision tasks Computing methodologies → Model verification and validation Effectiveness Human‐centered computing → Visual analytics Hypotheses Interactive systems and tools Machine learning Metadata Object recognition Subgroups |
| Title | VISLIX: An XAI Framework for Validating Vision Models with Slice Discovery and Analysis |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fcgf.70125 https://www.proquest.com/docview/3232402544 |
| Volume | 44 |
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