PAINE Demo: Optimizing Video Selection Queries With Commonsense Knowledge
Summary: PAINE builds a cheap lossy sampled-frame index and uses probabilistic models grounded in external commonsense knowledge to predict which unindexed videos satisfy semantic selection predicates, pruning irrelevant videos before running expensive detectors. Demo prototype lets users compare PAINE vs baseline SCAN. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Wenjia He
- 2. Ibrahim Sabek
- 3. Yuze Lou
- 4. Michael Cafarella
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 316 | NoScope: Optimizing Neural Network Queries over Video at Scale | 2017 | VLDB | 0.00027988668 |
| 329 | Accelerating Machine Learning Inference with Probabilistic Predicates | 2018 | SIGMOD | 0.00027249545 |
| 696 | BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics | 2020 | VLDB | 0.00018048935 |
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