Asymmetric Cross-Modal Hashing Based on Formal Concept Analysis
Authors: Yinan Li, Jun Long, Zhan Yang
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on MIRFlickr, NUS-WIDE and IAPR-TC12 datasets demonstrate the superior performance of ACHFCA to state-of-the-art hashing approaches. |
| Researcher Affiliation | Academia | Yinan Li, Jun Long, Zhan Yang* Big Data Institute, Central South University, Changsha, China EMAIL |
| Pseudocode | Yes | Algorithm 1: The optimization of ACHFCA Input: Training instances X(m), label matrix L, balance parameters γ, η, λ, ω, maximum iteration number ξ. Output: Binary codes B. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | In this paper, we use MIRFlickr, NUS-WIDE (Chua et al. 2009) and IAPR-TC12 (Escalante et al. 2010) datasets for evaluation. |
| Dataset Splits | Yes | For MIRFlickr dataset, we randomly choose 18,015 image-text pairs as the training set while the residual as the test set. For NUS-WIDE dataset, we select 10 typical tags and randomly choose 1,867 image-text pairs as the query set. For IAPR-TC12 dataset, we randomly divide the image-text pairs into 18,000/2,000 training/test sets. The statistics information of the three datasets are listed in Table 2. |
| Hardware Specification | Yes | All experiments are trialed on a server with Intel Xeon Silver 4210 Processor @2.20 GHz, 128G RAM. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For the proposed ACHFCA, parameters γ, η, λ, ω are selected by utilizing grid search (from 10 5 to 104, 10 times per step). We provide the best performance of parameter configurations in Table 3. In addition, ρ is set to 0.5. |