Disentangled Feature Learning Network for Vehicle Re-Identification
Authors: Yan Bai, Yihang Lou, Yongxing Dai, Jun Liu, Ziqian Chen, Ling-Yu Duan
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments show the effectiveness of our method that achieves state-of-the-art performance on three challenging datasets. We conduct experiments on Vehicle ID [Liu et al., 2016a], Ve RI-776 [Liu et al., 2016c] and VERI-Wild [Lou et al., 2019b] datasets, which are widely used vehicle Re ID benchmarks. |
| Researcher Affiliation | Academia | 1 National Engineering Lab for Video Technology, Peking University, Beijing, China 2 ISTD Pillar, Singapore University of Technology and Design, Singapore 3 Peng Cheng Laboratory, Shenzhen, China |
| Pseudocode | No | The paper describes algorithms verbally but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for the source code of its methodology. |
| Open Datasets | Yes | We conduct experiments on Vehicle ID [Liu et al., 2016a], Ve RI-776 [Liu et al., 2016c] and VERI-Wild [Lou et al., 2019b] datasets, which are widely used vehicle Re ID benchmarks. |
| Dataset Splits | No | The paper mentions 'In training stage' and 'During testing' but does not explicitly provide specific percentages or counts for training, validation, and test splits for the datasets used in a comprehensive manner for reproducibility. It only lists test sizes for some datasets. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Res Net50 [He et al., 2016]' as the backbone, but does not specify any software dependencies (e.g., Python version, deep learning framework, libraries) with version numbers. |
| Experiment Setup | Yes | Regarding parameters, we set ω as 0.5 and triplet margin as 0.6 in metric learning following [Lou et al., 2019b], and λ = 0.5 in hybrid ranking. The models are trained for 50 epochs. Learning rate starts from 0.003. The size of the input image is 256 256. |