Memory-oriented Decoder for Light Field Salient Object Detection
Authors: Miao Zhang, Jingjing Li, JI WEI, Yongri Piao, Huchuan Lu
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The success of our method is demonstrated by achieving the state of the art on three datasets. We present this problem in a way that is accessible to members of the community and provide a large-scale light field dataset that facilitates comparisons across algorithms. The code and dataset are made publicly available at https://github.com/OIPLab-DUT/MoLF. ... Extensive experiments on three light field datasets show that our method achieves consistently superior performance over 25 state-of-the-art 2D, 3D and 4D approaches. ... 5 Experiments ... To evaluate the performance of our proposed network, we conduct experiments on our proposed dataset and the only two public light field saliency datasets: LFSD [29] and HFUT [59]. |
| Researcher Affiliation | Academia | Miao Zhang Jingjing Li Wei Ji Yongri Piao Huchuan Lu Dalian University of Technology, China miaozhang@dlut.edu.cn, {lijingjing, jiwei521}@mail.dlut.edu.cn, {yrpiao, lhchuan}@dlut.edu.cn |
| Pseudocode | No | The paper describes methods using text and mathematical equations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and dataset are made publicly available at https://github.com/OIPLab-DUT/MoLF. |
| Open Datasets | Yes | To remedy the data deficiency problem, we introduce a large-scale light field saliency dataset with 1462 selected high-quality samples captured by Lytro Illum camera. ... The code and dataset are made publicly available at https://github.com/OIPLab-DUT/MoLF. |
| Dataset Splits | No | Ours: This dataset consists of 1462 light field samples. We randomly select 1000 samples for training and the remaining 462 samples for testing. |
| Hardware Specification | Yes | Our network is implemented on Pytorch framework and trained with a GTX 2080 Ti GPU. |
| Software Dependencies | No | The paper mentions 'Pytorch framework' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | All training and test images are uniformly resized to 256 256. Our network is trained in an end-to-end manner, in which the momentum, weight decay and learning rate are set to 0.9, 0.0005, 1e-10, respectively. During the training phrase, we use softmax entropy loss, and the network is trained by standard SGD and converges after 40 epochs with batch size of 1. |