Unsupervised Vehicle Re-identification with Progressive Adaptation
Authors: Jinjia Peng, Yang Wang, Huibing Wang, Zhao Zhang, Xianping Fu, Meng Wang
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experimental results validate the advantages of PAL on both Vehicle ID and Ve Ri-776 dataset. |
| Researcher Affiliation | Academia | 1College of Information and Science Technology, Dalian Maritime University, Liaoning, Dalian 2Pengcheng Laboratory, Shenzhen, Guangdong 3Key Laboratory of Knowledge Engineering with Big Data, Ministry of education, Hefei University of Technology, China 4School of Computer Science and Information Engineering, Hefei University of Technology, China |
| Pseudocode | Yes | Algorithm 1 PAL for Unsupervised Vehicle re ID |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The experiments are conducted over the following two typical data sets for Vehicle re ID: Ve Ri-776 and Vehicle ID. Ve Ri-776 [Liu et al., 2018] is a large-scale urban surveillance vehicle dataset for re ID... Vehicle ID [Liu et al., 2016a] is a surveillance dataset from the real-world scenario... |
| Dataset Splits | No | For Ve Ri-776, the paper specifies '37,781 images of 576 vehicles are employed as training set, while 11,579 images of 200 vehicles are employed as a test set.' For Vehicle ID, it mentions extracting subsets for testing. A distinct validation set or split is not explicitly mentioned. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, memory) are provided in the paper. It only mentions 'Considering the limit of device...' |
| Software Dependencies | No | The paper mentions 'trained in the tensorflow [Abadi et al., 2016]' and that 'the Res Net50 [He et al., 2015] is employed as the backbone network'. While software is mentioned, specific version numbers for TensorFlow or other libraries are not provided. |
| Experiment Setup | No | The 'Implementation Details' section describes some high-level aspects like using Cycle GAN and ResNet50, and the number of transferred images, but it does not specify concrete hyperparameters such as learning rate, batch size, number of epochs, or optimizer settings needed for reproducibility. |