Demonstrating TACT: Tunable Accuracy-Cost Trade-offs for Semantic Image Filtering
Summary: TACT interactively filters large image datasets with natural-language predicates via cascades of embedding and LLM operators. It profiles candidates and automatically selects Pareto-efficient plans under user-specified precision, recall, and monetary-cost preferences. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Tharushi Jayasekara (Cornell University)
- 2. Immanuel Trummer (Cornell University)
BibTeX Citation
@article{jayasekara_vldb26,
title = {{Demonstrating TACT: Tunable Accuracy-Cost Trade-offs for Semantic Image Filtering}},
author = {Jayasekara, Tharushi and Trummer, Immanuel},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4722--4725},
doi = {10.14778/3827998.3828106},
url = {https://doi.org/10.14778/3827998.3828106},
year = {2026}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 52 | The Snowflake Elastic Data Warehouse | 2016 | SIGMOD | 0.00041219077 |
| 669 | CAESURA: Language Models as Multi-Modal Query Planners | 2024 | CIDR | 0.0001495987 |
| 2,450 | ThalamusDB: Approximate Query Processing on Multi-Modal Data | 2024 | SIGMOD | 8.4474092e-05 |
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