Dual Self-Paced Cross-Modal Hashing
Authors: Yuan Sun, Jian Dai, Zhenwen Ren, Yingke Chen, Dezhong Peng, Peng Hu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on three widelyused benchmark datasets to demonstrate the effectiveness and robustness of the proposed DSCMH over 12 state-of-the-art CMH methods. |
| Researcher Affiliation | Academia | 1 College of Computer Science, Sichuan University, Chengdu, China 2 National Innovation Center for UHD Video Technology, Chengdu, China 3 Department of Automation, Tsinghua University, Beijing, China 4 School of National Defense Science and Technology, Southwest University of Science and Technology, Mianyang, China 5 Department of Computer and Information Sciences, Northumbria University, UK |
| Pseudocode | No | The paper describes the method using mathematical equations and steps, but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide concrete access to its own source code, such as a repository link or an explicit statement of code release. |
| Open Datasets | Yes | To evaluate the performance of our DSCMH, we compare it with thirteen baselines on three used-widely benchmark datasets, i.e., MIRFlickr, IAPR-TC12, and NUS-WIDE. |
| Dataset Splits | No | The paper specifies a 'query set' for evaluation but does not provide explicit training/validation/test dataset splits or mention a validation set for model tuning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | In the experiments, we empirically set m = 15, q = 1.2, and d = 1500. From the parameter analysis, we set α and λ as {10 3, 10 2}, {10 2, 10 3}, and {10 3, 10 4} on three datasets, respectively. |