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..
Enhanced-alignment Measure for Binary Foreground Map Evaluation
Authors: Deng-Ping Fan, Cheng Gong, Yang Cao, Bo Ren, Ming-Ming Cheng, Ali Borji
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the superiority of our measure over the available measures on 4 popular datasets via 5 meta-measures, including ranking models for applications, demoting generic, random Gaussian noise maps, ground-truth switch, as well as human judgments. |
| Researcher Affiliation | Academia | 1 College of Computer and Control Engineering, Nankai University 2 Center for Research in Computer Vision, Central Florida University |
| Pseudocode | No | The paper describes its method using mathematical equations and diagrams (e.g., Figure 4), but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | To help future explorations in this area, our code and dataset will be made publicly available on the web. |
| Open Datasets | Yes | The employed datasets include PASCAL-S [Li et al., 2014], ECSSD [Xie et al., 2013], HKUIS [Li and Yu, 2015], and SOD [Martin et al., 2001]. ... We name our dataset FMDatabase1 which contains 185 images. ... FMDatabase: http://dpfan.net/e-measure/ |
| Dataset Splits | No | The paper uses several datasets for evaluation but does not specify explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or cross-validation methodology). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run its experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions using 'LIRE' with 'CEDD' for application realization but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | No | The paper describes the experimental setup in terms of meta-measures and datasets used, but does not provide specific hyperparameters, model initialization details, or other system-level training configurations. |