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..
Salient Object Detection by Lossless Feature Reflection
Authors: Pingping Zhang, Wei Liu, Huchuan Lu, Chunhua Shen
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on seven saliency detection datasets demonstrate that our approach achieves consistently superior performance and outperforms the very recent state-of-the-art methods. |
| Researcher Affiliation | Academia | 1 Dalian University of Technology, Dalian, 116024, P.R. China 2 Shanghai Jiao Tong University, Shanghai, 200240, P.R. China 3 University of Adelaide, Adelaide, SA 5005, Australia |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is publicly available at http://ice.dlut.edu.cn/lu/. |
| Open Datasets | Yes | To train our model, we adopt the MSRA10K [Borji et al., 2015] dataset, which has 10,000 training images with high quality pixel-wise saliency annotations. |
| Dataset Splits | No | We do not use validation set and train the model until its training loss converges. |
| Hardware Specification | Yes | We train and test our method with an NVIDIA Titan 1070 GPU (8G memory) and an i5-6600 CPU. |
| Software Dependencies | No | The paper states: 'We implement our model based on the Caffe toolbox [Jia et al., 2014] with the MATLAB 2016 platform.' While MATLAB 2016 specifies a version, Caffe is not given a specific version number, only a citation. |
| Experiment Setup | Yes | The input image is uniformly resized into 384 384 3 pixels and subtracted the Image Net mean [Deng et al., 2009]... During the training, we use standard SGD method with batch size 12, momentum 0.9 and weight decay 0.0005. We set the base learning rate to 1e-8 and decrease the learning rate by 10% when training loss reaches a ο¬at. The training process converges after 150k iterations. |