Top-K Deep Video Analytics: A Probabilistic Approach
Summary: Everest enables efficient Top-K video analytics with probabilistic guarantees. Integrates deep CV models, uncertain data management, and Top-K query processing to rank top frames; yields 14.3x–20.6x efficiency on real videos and Visual Road. (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. Chenxia Han (Chinese University of Hong Kong)
- 3. Chris Liu (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_sigmod21,
title = {{Top-K Deep Video Analytics: A Probabilistic Approach}},
author = {Lai, Ziliang and Han, Chenxia and Liu, Chris and Zhang, Pengfei and Lo, Eric and Kao, Ben},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3452786},
url = {https://dl.acm.org/doi/10.1145/3448016.3452786},
year = {2021}
}
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,066 | Aero: Adaptive Query Processing of ML Queries | 2025 | SIGMOD | 5.712204e-05 |
| 9,353 | On Efficient Approximate Queries over Machine Learning Models | 2023 | VLDB | 5.2829539e-05 |
| 9,927 | Everest: A Top-K Deep Video Analytics System | 2022 | SIGMOD | 5.1955087e-05 |
| 10,476 | On Efficient Approximate Aggregate Nearest Neighbor Queries over Learned Representations | 2026 | SIGMOD | 5.093636e-05 |
| 10,791 | High-Throughput Ingestion for Video Warehouse: Comprehensive Configuration and Effective Exploration | 2025 | SIGMOD | 5.093636e-05 |
| 10,795 | Scalable Complex Event Processing on Video Streams | 2025 | SIGMOD | 5.093636e-05 |
| 11,162 | Predictive and Near-Optimal Sampling for View Materialization in Video Databases | 2024 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 16 of 16 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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