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 [1].
Deep Salience: Visual Salience Modeling via Deep Belief Propagation
Authors: Richard Jiang, Danny Crookes
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluation on the benchmark dataset validated that our Deep Salience model can consistently outperform eleven state-of-the-art salience models, yielding the higher rates in the precision-recall tests and attaining the best F-measure and mean-square error in the experiments. |
| Researcher Affiliation | Academia | Richard Jiang Automatic Control and System Engineering The University of Sheffield Sheffield, United Kingdom E-mail: EMAIL Danny Crookes ECIT Institute Queen s University Belfast Belfast, United Kingdom E-mail: EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We tested our method on the widely used object dataset --the EPFL object dataset (Achanta et al 2009), which is publicly available and the ground truth of foreground objects is provided as binary masks. |
| Dataset Splits | No | The paper mentions using the EPFL dataset but does not provide specific details on training, validation, and test splits needed to reproduce the data partitioning. It only states that 'The curves are computed in the same way as reported by previous work.' |
| Hardware Specification | No | The paper mentions that 'The computing time was measured for a MATLAB solution.' but does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper states 'Our algorithm was implemented in MATLAB.' but does not provide specific version numbers for MATLAB or any other ancillary software dependencies. |
| Experiment Setup | No | The paper mentions some setup details such as using the SF method for initialization and assuming border pixels as background, but it does not provide specific experimental setup details like concrete hyperparameter values, training configurations, or system-level settings. |