This is simple python re-implementation of the algorithms from papers Iscen.et.al "Fast Spectral Ranking for Similarity Search", CVPR2018 and Iscen et.al "Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations" CVPR 2017.
It is NOT authors implementation and some parts, e.g. sparsification, truncation, etc. are missing.
Example of usage: copy files into python directory of the RevisitOP benchmark and run
python example_evaluate_with_diff.py
Expected output:
Plain
>> roxford5k: mAP E: 84.81, M: 64.67, H: 38.47
>> roxford5k: mP@k[ 1 5 10] E: [ 97.06 85.29 70.59], M: [ 97.14 82.86 64.29], H: [ 81.43 31.43 22.86]
Conjugate gradient
>> roxford5k: mAP E: 86.42, M: 72.52, H: 48.56
>> roxford5k: mP@k[ 1 5 10] E: [ 92.65 91.18 82.35], M: [ 92.86 87.14 75.71], H: [ 87.14 41.43 27.14]
Spectral K=100, R=2000
>> roxford5k: mAP E: 86.5, M: 72.0, H: 45.7
>> roxford5k: mP@k[ 1 5 10] E: [ 94.12 91.18 80.88], M: [ 94.29 82.86 70. ], H: [ 81.43 41.43 22.86]
