SDMCH: Supervised Discrete Manifold-Embedded Cross-Modal Hashing
Authors: Xin Luo, Xiao-Ya Yin, Liqiang Nie, Xuemeng Song, Yongxin Wang, Xin-Shun Xu
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on three benchmark datasets demonstrate that SDMCH outperforms ten state-of-the-art cross-modal hashing methods. To evaluate the performance of SDMCH, we conducted extensive experiments on three benchmark datasets and compared it with ten state-of-the-art hashing methods. |
| Researcher Affiliation | Academia | School of Computer Science and Technology, Shandong University School of Software, Shandong University {luoxin.lxin, xysyin, nieliqiang,sxmustc}@gmail.com, yxinwang@hotmail.com, xuxinshun@sdu.edu.cn |
| Pseudocode | Yes | Algorithm 1 Optimization algorithm in SDMCH. |
| Open Source Code | No | The paper states: "The source codes of most baselines are kindly provided by the authors." This refers to baselines, not the code for SDMCH itself. There is no explicit statement or link for the SDMCH code. |
| Open Datasets | Yes | Experiments are conducted on three benchmark datasets, i.e., Wiki [Rasiwasia et al., 2010], MIRFlickr [Huiskes and Lew, 2008], and NUS-WIDE [Chua et al., 2009], which are described below. |
| Dataset Splits | Yes | Wiki consists of 2, 866 image-text pairs collected from Wikipedia... It was initially split into a training set with 2, 173 instances and a test set of 693 instances. MIRFlickr consists of 25, 000 instances collected from Flickr website... We randomly selected 25% instances of the dataset as the query set, and the remaining 75% ones as the training set. NUS-WIDE contains 269, 648 instances crawled from Flickr... We select 2% of the data as the query set and the rest as the training set. |
| Hardware Specification | No | No information about hardware specifications is provided. |
| Software Dependencies | No | The paper mentions "source codes of most baselines" but does not specify any software versions for their own implementation (e.g., Python, specific libraries). |
| Experiment Setup | Yes | For SDMCH, its parameters are set to K = 6, λ1 = 0.3, λ2 = 0.7, α = 0.3, β = 5, γ = 1000 and θ = 1, selected by a validation procedure. In addition, the total iterative number T in Algorithm 1 is set to 10. |