Dihn
source code for paper "Deep Incremental Hashing Network for Efficient Image Retrieval" on CVPR-2019
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Hashing Baseline For Image Retrieval | 563 | 2 years ago | 4 | MATLAB | ||||||
| :octocat:Various hashing methods for image retrieval and serves as the baselines | ||||||||||
| Caffe Deepbinarycode | 207 | 7 years ago | 8 | other | C++ | |||||
| Supervised Semantics-preserving Deep Hashing (TPAMI18) | ||||||||||
| Deepsketchhashing | 96 | 7 years ago | 3 | |||||||
| Deep Sketch Hashing source code | ||||||||||
| Orthohash | 71 | a year ago | bsd-3-clause | Python | ||||||
| Official implementation of NeurIPS 2021 paper "One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective" | ||||||||||
| Bitscalabledeephash | 65 | 9 years ago | 2 | C++ | ||||||
| The Code of "Bit-Scalable Deep Hashing with Regularized Similarity Learning for Image Retrieval and Person Re-identification." IEEE TIP2015 | ||||||||||
| Gpq | 59 | 2 years ago | mit | Python | ||||||
| Generalized Product Quantization Network For Semi-supervised Image Retrieval - CVPR 2020 | ||||||||||
| Cisip Fire | 57 | 8 months ago | bsd-3-clause | Python | ||||||
| Fast Image Retrieval (FIRe) is an open source project to promote image retrieval research. It implements most of the major binary hashing methods to date, together with different popular backbone networks and public datasets. | ||||||||||
| Deepembeding | 54 | 4 years ago | 1 | Jupyter Notebook | ||||||
| 图像检索和向量搜索，similarity learning,compare deep metric and deep-hashing applying in image retrieval | ||||||||||
| Massimageretrieval | 47 | 6 years ago | apache-2.0 | Python | ||||||
| This project is intended to solve the task of massive image retrieval. | ||||||||||
| Talr | 43 | 3 years ago | 1 | other | MATLAB | |||||
| Code for CVPR 2018 paper "Hashing as Tie-Aware Learning to Rank" | ||||||||||
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