Demonstration of VCR: A Tabular Data Slicing Approach to Understanding Object Detection Model Performance
Summary: VCR converts vision-foundation-model segment concepts and image metadata into tabular data for scalable frequent-itemset slice discovery in object-detection errors. An interactive workflow supports visual inspection and feedback-driven refinement of concept granularity and labels. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Jie Jeff Xu (Georgia Institute of Technology)
- 2. Saahir Dhanani (Georgia Institute of Technology)
- 3. Jorge Piazentin Ono (Bosch Center for AI)
- 4. Wenbin He (Bosch Center for AI)
- 5. Liu Ren (Bosch Center for AI)
- 6. Kexin Rong (Georgia Institute of Technology)
BibTeX Citation
@article{xu_vldb24,
title = {{Demonstration of VCR: A Tabular Data Slicing Approach to Understanding Object Detection Model Performance}},
author = {Xu, Jie Jeff and Dhanani, Saahir and Ono, Jorge Piazentin and He, Wenbin and Ren, Liu and Rong, Kexin},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {12},
pages = {4453--4456},
doi = {10.14778/3685800.3685898},
url = {https://doi.org/10.14778/3685800.3685898},
year = {2024}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 27 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00052088039 |
| 3,204 | Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence | 2021 | SIGMOD | 7.6212389e-05 |
| 7,537 | POEM: Pattern-Oriented Explanations of Convolutional Neural Networks | 2023 | VLDB | 5.5795657e-05 |
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