Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification
Authors: Zheng Wang, Mang Ye, Fan Yang, Xiang Bai, Shin'ichi Satoh
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations on two simulated datasets and one public dataset demonstrate the advantages of our method over related state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1 National Institute of Informatics, Japan 2 Hong Kong Baptist University, China 3 The University of Tokyo, Japan 4 Huazhong University of Science of Technology, China |
| Pseudocode | No | The paper includes architectural diagrams (e.g., Figure 2) but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating the availability of its source code. |
| Open Datasets | Yes | Following [Wang et al., 2016b], the evaluation is run on two simulated person datasets SALR-VIPe R and SALR-PRID, which are based on the VIPe R dataset [Gray et al., 2007] and the PRID450S dataset [Roth et al., 2014] respectively, and the public CAVIAR dataset [Cheng et al., 2011]. |
| Dataset Splits | Yes | Following [Wang et al., 2016b], all datasets are randomly divided into training set and testing set. Persons for training and testing are respectively 532 and 100 (SALR-VIPe R), 400 and 50 (SALR-PRID), and 44 and 10 (CAVIAR). |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions deep learning models like ResNet-50 and VGG network but does not specify any software libraries or their version numbers (e.g., TensorFlow, PyTorch, Python versions). |
| Experiment Setup | Yes | The training process includes the following three steps: (1) We first initialize the re-identification network separately. We choose Res Net-50 [He et al., 2016] as the base. The Res Net50 is pre-trained with Image Net [Russakovsky et al., 2015], and then fine-tuned with the Market-1501 [Zheng et al., 2015] dataset. (2) The cascaded generator networks are initialized with MSE losses. (3) The whole network is trained simultaneously with all the losses. ... following [Ledig et al., 2016], we set α = 2 * 10^-6 and β = 1 * 10^-3. |