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 | Conference PDF | Archive PDF | Plain Text | 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. |