Demonstration of VCR: A Tabular Data Slicing Approach to Understanding Object Detection Model Performance
Summary: VCR converts segment-level concepts from vision foundation models into tabular, interpretable features combined with image metadata to enable slice discovery for object detection. It uses scalable frequent-itemset mining to find systematic failure slices and supports iterative refinement. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Jie Jeff Xu
- 2. Saahir Dhanani
- 3. Jorge Piazentin Ono
- 4. Wenbin He
- 5. Liu Ren
- 6. Kexin Rong
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| 36 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00076161096 |
| 3,162 | Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence | 2021 | SIGMOD | 7.4589576e-05 |
| 6,903 | POEM: Pattern-Oriented Explanations of Convolutional Neural Networks | 2023 | VLDB | 4.8925595e-05 |
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