LotusFilter: Diversity-aware approximate nearest neighbor search combining Faiss with cutoff tables to prevent similar results from dominating search outputs.
✨ Features
- ⚡ Lightweight: Ultra-fast processing < 0.1ms per query
- 🔓 Independence: Post-processing module that doesn't require original data retention
- 🔧 Flexibility: Works seamlessly with various Faiss indexes
- 📦 Simplicity: Single
CutoffTableclass - easy to integrate
🚀 Quick Start
Installation
pip install lotf
Simple Usage
import lotf
import faiss
import numpy as np
# Prepare data
Xb = np.random.rand(10000, 128).astype('float32') # Database vectors
Xq = np.random.rand(5, 128).astype('float32') # Query vectors
# Step 1: Build Faiss index
index = faiss.IndexFlatL2(Xb.shape[1])
index.add(Xb)
# Step 2: Build cutoff table for diversity filtering
epsilon = 15.0
ctable = lotf.CutoffTable(X=Xb, index=index, epsilon=epsilon)
# Step 3: Search candidates
candidate_k = 300
candidate_dists, candidate_ids = index.search(Xq, candidate_k)
# Step 4: Filter out candidates to obtain diverse results
final_k = 100
diverse_dists, diverse_ids = ctable.filter(
dists=candidate_dists,
ids=candidate_ids,
final_k=final_k
)
print(f"Diverse results: {diverse_ids}")
📄 Citation
If you use LotusFilter in your research, please cite our CVPR 2025 paper:
@inproceedings{mtasui2025cvpr,
author = {Yusuke Matsui},
title = {LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2025},
pages = {30430-30439}
}
📚 Documentation
- Repository: https://github.com/matsui528/lotf
- Documentation: https://matsui528.github.io/lotf
- Paper: https://www.arxiv.org/abs/2506.04790
👤 Author
Yusuke Matsui
- Email: matsui@hal.t.u-tokyo.ac.jp
- GitHub: @matsui528
- Web: https://yusukematsui.me
1 maintainer
Metadata
1 maintainer
Release files for lotf 0.0.4
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