ducha-aiki / Manifold Diffusion
Licence: mit
Diffusion on manifolds for image retrieval
Stars: ✭ 102
Programming Languages
python
139335 projects - #7 most used programming language
Labels
Projects that are alternatives of or similar to Manifold Diffusion
Mxnet Ir
Image Retrieval Experiment Using Triplet Loss
Stars: ✭ 27 (-73.53%)
Mutual labels: image-retrieval
Gss
Code for the NeurIPS'19 paper "Guided Similarity Separation for Image Retrieval"
Stars: ✭ 54 (-47.06%)
Mutual labels: image-retrieval
Vehicle Retrieval Kcnns
vehicle image retrieval using k CNNs ensemble method
Stars: ✭ 81 (-20.59%)
Mutual labels: image-retrieval
Mirror
Matchable Image Retrieval by Learning from Surface Reconstruction
Stars: ✭ 44 (-56.86%)
Mutual labels: image-retrieval
Massimageretrieval
This project is intended to solve the task of massive image retrieval.
Stars: ✭ 47 (-53.92%)
Mutual labels: image-retrieval
Awesome Cbir Papers
📝Awesome and classical image retrieval papers
Stars: ✭ 1,114 (+992.16%)
Mutual labels: image-retrieval
Cbir System
Content-Based Image Retrieval system (KTH DD2476 Project)
Stars: ✭ 9 (-91.18%)
Mutual labels: image-retrieval
Imageretrieval Tf
基于tensorflow & tf-servering & flask 的图像检索
Stars: ✭ 94 (-7.84%)
Mutual labels: image-retrieval
Dg Net
Joint Discriminative and Generative Learning for Person Re-identification. CVPR'19 (Oral)
Stars: ✭ 1,042 (+921.57%)
Mutual labels: image-retrieval
Image similarity
PyTorch Blog Post On Image Similarity Search
Stars: ✭ 80 (-21.57%)
Mutual labels: image-retrieval
Deep Fashion
Proposal a new method to retrieval clothing images
Stars: ✭ 44 (-56.86%)
Mutual labels: image-retrieval
Keras rmac plus
Keras implementation of R-MAC+ descriptors
Stars: ✭ 46 (-54.9%)
Mutual labels: image-retrieval
Deep Ranking
Learning Fine-grained Image Similarity with Deep Ranking is a novel application of neural networks, where the authors use a new multi scale architecture combined with a triplet loss to create a neural network that is able to perform image search. This repository is a simplified implementation of the same
Stars: ✭ 64 (-37.25%)
Mutual labels: image-retrieval
Hierarchical Localization
Visual localization made easy with hloc
Stars: ✭ 997 (+877.45%)
Mutual labels: image-retrieval
Deepembeding
图像检索和向量搜索，similarity learning,compare deep metric and deep-hashing applying in image retrieval
Stars: ✭ 83 (-18.63%)
Mutual labels: image-retrieval
Deep Mihash
Code for papers "Hashing with Mutual Information" (TPAMI 2019) and "Hashing with Binary Matrix Pursuit" (ECCV 2018)
Stars: ✭ 13 (-87.25%)
Mutual labels: image-retrieval
Digix cv time top3
2020华为DIGIX全球校园AI算法精英大赛计算机视觉赛道第三名解决方案
Stars: ✭ 58 (-43.14%)
Mutual labels: image-retrieval
Trace.moe Webextension
WebExtension for the Anime Reverse Search Engine to search by image
Stars: ✭ 89 (-12.75%)
Mutual labels: image-retrieval
Adsh Aaai2018
source code for paper "Asymmetric Deep Supervised Hashing" on AAAI-2018
Stars: ✭ 67 (-34.31%)
Mutual labels: image-retrieval
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: [email protected][ 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: [email protected][ 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: [email protected][ 1 5 10] E: [ 94.12 91.18 80.88], M: [ 94.29 82.86 70. ], H: [ 81.43 41.43 22.86]
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].