NoScope: Optimizing Neural Network Queries over Video at Scale
Summary: Auto-sequences a cascade of specialized and difference detectors to accelerate neural-network video queries while preserving reference-model accuracy. A cost-based optimizer tunes the cascade per video/object, delivering up to 15,500x real-time speedups with 1–5% accuracy loss. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Daniel Kang
- 2. John Emmons
- 3. Firas Abuzaid
- 4. Peter Bailis
- 5. Matei Zaharia
Incoming Citations (Sorted by Pagerank)
Showing 5 of 55 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,675 | Déjà Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse | 2025 | VLDB | 4.1905499e-05 |
| 11,275 | PAINE Demo: Optimizing Video Selection Queries With Commonsense Knowledge | 2023 | VLDB | 4.1905499e-05 |
| 11,429 | Accelerating Queries over Unstructured Data with ML | 2021 | CIDR | 4.1905499e-05 |
| 11,487 | Shahin: Faster Algorithms for Generating Explanations for Multiple Predictions | 2021 | SIGMOD | 4.1905499e-05 |
| 11,748 | Collaborative Edge and Cloud Neural Networks for Real-Time Video Processing | 2018 | VLDB | 4.1905499e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 15 of 15 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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