Optimized Product Quantization and Locally Optimized Product Quantization
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Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 20/100
Research direction
The issue names Product Quantization, Optimized Product Quantization, and Locally Optimized Product Quantization, but no repository files, tests, or entry points. Start by locating the existing indexing and retrieval implementation and comparing its scope with the linked papers; completion criteria are not specified in the issue.
Written by the indexing model from the issue text.
Description
In 2013, there are two important improvements of Product Quantization. Optimized Product Quantization non-parametric solution [2] was equivalent to the Cartesian k-means [1] and performed better than PQ. In 2014, Locally Optimized Product Quantization [3] further improved upon OPQ.
SIFT1B with 64-bit codes, K=213=8192 and w=64. For Multi-D-ADC, K=214 and T=100K.
SIFT1B with 128-bit codes and K=213=8192 (resp. K=214) for single index (resp. multi-index). For IVFADC+R and LOPQ+R, m′=8, w=64.
- Mohammad Norouzi, David J. Fleet. Cartesian k-means. IEEE Computer Vision and Pattern Recognition (CVPR), 2013.
- Optimized Product Quantization, by Tiezheng Ge, Kaiming He, Qifa Ke, and Jian Sun, in TPAMI.
- Y. Kalantidis, Y. Avrithis. Locally Optimized Product Quantization for Approximate Nearest Neighbor Search. In Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR 2014), Columbus, Ohio, June 2014.
- Dominant language
- Java
- Stars
- 63
- Forks
- 19
- PR merge metrics
- No merged PRs in 30d
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