Salient Object Detection by Lossless Feature Reflection
Authors: Pingping Zhang, Wei Liu, Huchuan Lu, Chunhua Shen
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | 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 flat. The training process converges after 150k iterations. |