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..
Asymmetric Cross-Modal Hashing Based on Formal Concept Analysis
Authors: Yinan Li, Jun Long, Zhan Yang
AAAI 2025 | Venue PDF | 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. |