Everest: A Top-K Deep Video Analytics System
Summary: Everest enables efficient Top-K video analytics with probabilistic guarantees to surface the most interesting frames/clips. It supports user-defined ranking via multiple deep vision models, blending CV, uncertain databases, and Top-K query processing. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ziliang Lai
- 2. Chris Liu
- 3. Chenxia Han
- 4. Pengfei Zhang
- 5. Eric Lo
- 6. Ben Kao
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,080 | Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines | 2024 | VLDB | 4.5911668e-05 |
| 10,667 | Déjà Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse | 2025 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 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 |
| 1,388 | MIRIS: Fast Object Track Queries in Video | 2020 | SIGMOD | 0.00012260926 |
| 2,533 | DeepLens: Towards a Visual Data Management System | 2019 | CIDR | 8.5899934e-05 |
| 3,558 | Approximate Selection with Guarantees using Proxies | 2020 | VLDB | 6.9765724e-05 |
| 4,950 | Evaluating Temporal Queries Over Video Feeds | 2021 | SIGMOD | 5.8104133e-05 |
| 5,135 | Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning | 2022 | SIGMOD | 5.6724721e-05 |
| 6,182 | Top-K Deep Video Analytics: A Probabilistic Approach | 2021 | SIGMOD | 5.1682689e-05 |
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