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@inproceedings{2304817,
author = {Míč, Vladimír and Sedmidubský, Jan and Zezula, Pavel},
address = {Cham},
booktitle = {16th International Conference on Similarity Search and Applications (SISAP)},
doi = {http://dx.doi.org/10.1007/978-3-031-46994-7_26},
editor = {Oscar Pedreira, Vladimir Estivill-Castro},
keywords = {approximate similarity searching;high-dimensional data;indexing;filtering;LAION dataset},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Cham},
isbn = {978-3-031-46993-0},
pages = {300-308},
publisher = {Springer},
title = {CRANBERRY: Memory-Effective Search in 100M High-Dimensional CLIP Vectors},
url = {https://link.springer.com/chapter/10.1007/978-3-031-46994-7_26},
year = {2023}
}
TY - JOUR
ID - 2304817
AU - Míč, Vladimír - Sedmidubský, Jan - Zezula, Pavel
PY - 2023
TI - CRANBERRY: Memory-Effective Search in 100M High-Dimensional CLIP Vectors
PB - Springer
CY - Cham
SN - 9783031469930
KW - approximate similarity searching;high-dimensional data;indexing;filtering;LAION dataset
UR - https://link.springer.com/chapter/10.1007/978-3-031-46994-7_26
N2 - Recent advances in cross-modal multimedia data analysis necessarily require efficient similarity search on the scales of hundreds of millions of high-dimensional vectors. We address this task by proposing the CRANBERRY algorithm that specifically combines and tunes several existing similarity search strategies. In particular, the algorithm: (1) employs the Voronoi partitioning to obtain a query-relevant candidate set in constant time, (2) applies filtering techniques to prune the obtained candidates significantly, and (3) re-rank the retained candidate vectors with respect to the query vector. Applied to the dataset of 100 million 768-dimensional vectors, the algorithm evaluates 10NN queries with 90% recall and query latency of 1.2s on average, all with a throughput of 15 queries per second on a server with 56 core-CPU, and 4.7q/sec. on a PC.
ER -
MÍČ, Vladimír, Jan SEDMIDUBSKÝ a Pavel ZEZULA. CRANBERRY: Memory-Effective Search in 100M High-Dimensional CLIP Vectors. Online. In Oscar Pedreira, Vladimir Estivill-Castro. \textit{16th International Conference on Similarity Search and Applications (SISAP)}. Cham: Springer, 2023, s.~300-308. ISBN~978-3-031-46993-0. Dostupné z: https://dx.doi.org/10.1007/978-3-031-46994-7\_{}26.
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