Robust Self-Paced Hashing for Cross-Modal Retrieval with Noisy Labels
Authors: Ruitao Pu, Yuan Sun, Yang Qin, Zhenwen Ren, Xiaomin Song, Huiming Zheng, Dezhong Peng
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that the proposed RSHNL performs remarkably well over the state-of-the-art CMH methods. To verify the effectiveness of the proposed RSHNL, we conduct extensive experiments on four widely used datasets, i.e., XMedia (Peng et al. 2015), INRIA-Websearch (Krapac et al. 2010), Wikipedia (Rasiwasia et al. 2010), and XMedia Net (Peng, Huang, and Zhao 2018). |
| Researcher Affiliation | Collaboration | 1Sichuan University, Chengdu, China, 610044, 2Southwest University of Science and Technology, Mianyang, China, 621010, 3Sichuan National Innovation New Vision UHD Video Technology Co., Ltd., Chengdu, China, 610095 |
| Pseudocode | No | The training process of RSHNL is shown in the appendix. (No pseudocode or algorithm block is present in the main text of the paper.) |
| Open Source Code | Yes | Code https://github.com/perquisite/RSHNL |
| Open Datasets | Yes | Dataset To verify the effectiveness of the proposed RSHNL, we conduct extensive experiments on four widely used datasets, i.e., XMedia (Peng et al. 2015), INRIA-Websearch (Krapac et al. 2010), Wikipedia (Rasiwasia et al. 2010), and XMedia Net (Peng, Huang, and Zhao 2018). |
| Dataset Splits | No | The paper mentions using a "testing set" and "validation set" but does not provide specific percentages, sample counts, or explicit splitting methodology for these datasets in the main text. More details on implementation are provided in the appendix due to space limitations. |
| Hardware Specification | Yes | Besides, all experiments are conducted on a single Ge Force RTX3090Ti 24GB GPU and our RSHNL is implemented in Py Torch 1.12.0. |
| Software Dependencies | Yes | our RSHNL is implemented in Py Torch 1.12.0. |
| Experiment Setup | No | where α is a hyper-parameter, t is the current epoch, Nw and Nm are warm-up epoch and maximal epoch, respectively. (The paper mentions hyperparameters like alpha, Nw, and Nm but does not provide their specific values in the main text. It states that more implementation details are in the appendix.) |