Deep Evidential Hashing for Trustworthy Cross-Modal Retrieval
Authors: Yuan Li, Liangli Zhen, Yuan Sun, Dezhong Peng, Xi Peng, Peng Hu
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the efficacy of our DECH through extensive experimentation on four benchmark datasets. The experimental results demonstrate our superior performance compared to 12 state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1College of Computer Science, Sichuan University 2Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore 3Sichuan National Innovation New Vision UHD Video Technology Co., Ltd., Chengdu 610095, China |
| Pseudocode | Yes | Algorithm 1: The Optimization Procedure of DECH |
| Open Source Code | Yes | Code https://github.com/blackant-dev/DECH |
| Open Datasets | Yes | We conduct our experiments on four benchmark datasets: MIRFLICKR25K(Huiskes and Lew 2008), IAPR TC12(Escalante et al. 2010), NUS-WIDE(Rasiwasia et al. 2010), and MS-COCO(Lin et al. 2014). |
| Dataset Splits | Yes | MIRFLICKR25K contains 20,500 image-text pairs from 24 classes, with 2,000 pairs reserved for querying, 10,000 for training, and the rest for retrieval. IAPR TC-12 comprises 20,000 pairs across 255 categories, with 2,000 pairs used for querying, 10,000 for training, and the remainder for retrieval. NUS-WIDE includes 195,834 pairs in 21 categories, with 2,000 pairs for querying, 10,500 for training, and the rest for retrieval. MS-COCO consists of 122,218 pairs in 80 classes, with 5,000 pairs for querying, 10,000 for training, and the remaining pairs forming the retrieval database. |
| Hardware Specification | Yes | Our method is implemented with Py Torch(Paszke et al. 2019) on a single NVIDIA GEFORCE RTX 3090 Ti GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch(Paszke et al. 2019)' but does not specify a version number for PyTorch or any other software library. |
| Experiment Setup | Yes | For our DECH, we set τ to 0.2 and γ to 1. The parameter λ is empirically determined as per (Sensoy, Kaplan, and Kandemir 2018). Additionally, we also employ Lnzce RM as the near-zero correct-evidence loss during training. ... The optimal retrieval performance is achieved when τ is around 0.1. |