Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Deep Unified Cross-Modality Hashing by Pairwise Data Alignment
Authors: Yimu Wang, Bo Xue, Quan Cheng, Yuhui Chen, Lijun Zhang
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three representative image-text datasets demonstrate the superiority of our DUCMH over several state-of-the-art cross-modality hashing methods. |
| Researcher Affiliation | Academia | National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China EMAIL |
| Pseudocode | Yes | Algorithm 1 The alternative learning algorithm |
| Open Source Code | No | No explicit statement or link providing access to open-source code was found. |
| Open Datasets | Yes | Three datasets, MIRFLICKR-25K [Huiskes and Lew, 2008], IAPR TC-12 [Escalante et al., 2010], and NUS-WIDE [Chua et al., 2009] are used for evaluation. |
| Dataset Splits | No | For MIRFLICKR-25K and IAPR TC-12, 2,000 data points are randomly sampled as the test (query) set, while for NUS-WIDE, 2,100 data points are selected. The remaining points as the retrieval set (database). |
| Hardware Specification | Yes | Our DUCMH method is implemented based on Py Torch [Paszke et al., 2019] with eight NVIDIA V100 GPUs and optimized by the mini-batch SGD with the size of 64 and weight decay. |
| Software Dependencies | No | The paper mentions 'Py Torch [Paszke et al., 2019]' but does not specify a version number for it or other software dependencies. |
| Experiment Setup | Yes | Our DUCMH method is implemented based on Py Torch [Paszke et al., 2019] with eight NVIDIA V100 GPUs and optimized by the mini-batch SGD with the size of 64 and weight decay. The learning rate is initialized as 0.0001 for the image to text mapping fi2t( ) and 0.004 for the unified hash function hy( ). Hyper-parameters ϵ, α and ρ are empirically set to 5000, 50 and 200 for scaling the order of each loss. |