Dual Semantic Fusion Hashing for Multi-Label Cross-Modal Retrieval
Authors: Kaiming Liu, Yunhong Gong, Yu Cao, Zhenwen Ren, Dezhong Peng, Yuan Sun
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three benchmarks demonstrate the superior performance of our DSFH compared with 16 state-of-the-art methods. |
| Researcher Affiliation | Academia | Kaiming Liu1 , Yunhong Gong1 , Yu Cao1 , Zhenwen Ren2 , Dezhong Peng1,3 and Yuan Sun1; 1College of Computer Science, Sichuan University 2School of National Defense Science and Technology, Southwest University of Science and Technology 3National Innovation Center for UHD Video Technology |
| Pseudocode | No | The paper describes the optimization steps in text and mathematical equations, but no formal pseudocode block or algorithm box is provided. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | To evaluate the effectiveness of the proposed DSFH, we conduct numerous experiments on three benchmarks. MIRFlickr-25K [Huiskes and Lew, 2008] has 25,000 image-text pairs sourced from the Flickr website... IAPRTC12 [Escalante et al., 2010] consists of 20,000 geographical images belonging to 255 categories... NUS-WIDE [Chua et al., 2009] contains 269,648 image-text pairs... |
| Dataset Splits | Yes | In our experiments, we select the instances associated with a minimum of 20 textual labels, resulting in 20,015 instances. Further, we randomly choose 2,000 instances as the query set, while the remaining image-text pairs constitute the training set. |
| Hardware Specification | No | To comprehensively evaluate the retrieval performance, we conduct extensive experiments on a Windows server equipped with 64GB of RAM. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) were listed in the paper. |
| Experiment Setup | Yes | The number of anchors for RBF is set to 1500, and the maximum iteration step is set to 10. The hyper-parameters α and λ are set to {10 3, 10 3}, {10 4, 10 3}, and {10 4, 10 4} for MIRFlickr-25K, IAPRTC12, and NUS-WIDE, respectively. In addition, the number of clusters k is 400, 300, and 400 for three datasets, respectively. |