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 (Chinese University of Hong Kong)
- 2. Chris Liu (Chinese University of Hong Kong)
- 3. Chenxia Han (Chinese University of Hong Kong)
- 4. Pengfei Zhang (Chinese University of Hong Kong)
- 5. Eric Lo (Chinese University of Hong Kong)
- 6. Ben Kao (University of Hong Kong)
BibTeX Citation
@inproceedings{lai_sigmod22,
title = {{Everest: A Top-K Deep Video Analytics System}},
author = {Lai, Ziliang and Liu, Chris and Han, Chenxia and Zhang, Pengfei and Lo, Eric and Kao, Ben},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3520151},
url = {https://dl.acm.org/doi/10.1145/3514221.3520151},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,978 | Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines | 2024 | VLDB | 5.4136835e-05 |
| 11,310 | Deja Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse | 2025 | VLDB | 4.9793485e-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 |
|---|---|---|---|---|
| 281 | Accelerating Machine Learning Inference with Probabilistic Predicates | 2018 | SIGMOD | 0.00022295232 |
| 541 | BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics | 2020 | VLDB | 0.00016657685 |
| 1,024 | MIRIS: Fast Object Track Queries in Video | 2020 | SIGMOD | 0.00012430491 |
| 2,391 | DeepLens: Towards a Visual Data Management System | 2019 | CIDR | 8.5365368e-05 |
| 2,776 | Approximate Selection with Guarantees using Proxies | 2020 | VLDB | 8.0309448e-05 |
| 4,495 | Evaluating Temporal Queries Over Video Feeds | 2021 | SIGMOD | 6.5769374e-05 |
| 4,775 | Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning | 2022 | SIGMOD | 6.4193428e-05 |
| 5,804 | Top-K Deep Video Analytics: A Probabilistic Approach | 2021 | SIGMOD | 5.9888455e-05 |
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