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By integrating these visual concepts with additional image metadata in a tabular format, VCR uses a scalable frequent itemset mining-based technique to identify common patterns associated with model performance. We will demonstrate VCR's capabilities through three usage scenarios. First, users can explore the automatically extracted visual concepts and their associated labels. Second, users can run slice finding on a large object detection dataset and visually inspect the results to discover systematic errors. Finally, users can iteratively refine their slicing results by providing feedback on the granularity of visual concepts and the quality of the generated labels. These scenarios will illustrate how VCR can aid practitioners in discovering non-trivial gaps in their models' performance, providing actionable insights for model improvement.<\/jats:p>","DOI":"10.14778\/3685800.3685898","type":"journal-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T17:25:21Z","timestamp":1731086721000},"page":"4453-4456","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Demonstration of VCR: A Tabular Data Slicing Approach to Understanding Object Detection Model Performance"],"prefix":"10.14778","volume":"17","author":[{"given":"Jie Jeff","family":"Xu","sequence":"first","affiliation":[{"name":"Georgia Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saahir","family":"Dhanani","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jorge Piazentin","family":"Ono","sequence":"additional","affiliation":[{"name":"Bosch Research North America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbin","family":"He","sequence":"additional","affiliation":[{"name":"Bosch Research North America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liu","family":"Ren","sequence":"additional","affiliation":[{"name":"Bosch Research North America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kexin","family":"Rong","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proc. 20th int. conf. very large data bases, VLDB","volume":"1215","author":"Agrawal Rakesh","year":"1994","unstructured":"Rakesh Agrawal, Ramakrishnan Srikant, et al. 1994. 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