Dual Distribution Alignment Network for Generalizable Person Re-Identification
Authors: Peixian Chen, Pingyang Dai, Jianzhuang Liu, Feng Zheng, Mingliang Xu, Qi Tian, Rongrong Ji1054-1062
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method in a large-scale DG Re-ID benchmark and compare it with various cutting-edge DG approaches. Quantitative results show that DDAN achieves state-of-the-art performance. and 4 Experiments section with sub-sections like 4.2 Comparison with State-of-the-Arts and 4.5 Ablation Study. |
| Researcher Affiliation | Collaboration | Peixian Chen1, Pingyang Dai1 , Jianzhuang Liu3, Feng Zheng2, Mingliang Xu4, Qi Tian5, Rongrong Ji1,6 1 Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University 2 Department of Computer Science and Engineering, Southern University of Science and Technology 3 Noah s Ark Lab, Huawei Tech 4 School of Information Engineering, Zhengzhou University 5 Cloud & AI, Huawei Tech. 6 Institute of Artificial Intelligence, Xiamen University |
| Pseudocode | No | The paper describes its method using textual explanations and mathematical equations, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We conduct experiments on the large-scale DG Re-ID benchmark (Song et al. 2019) to evaluate our DG model for person Re-ID. Specifically, CUHK02 (Li and Wang 2013), CUHK03 (Li et al. 2014), Market-1501 (Zheng et al. 2015), Duke MTMC-Re ID (Zheng, Zheng, and Yang 2017) and CUHK-SYSU Person Search (Xiao et al. 2016) are taken as the source datasets. |
| Dataset Splits | No | The paper states that "All images in these source datasets, regardless of their train/test splits, are used for training, in total 121,765 images of 18,530 identities." This indicates that the entire source data is used for training, and no explicit train/validation/test splits are provided for the training data itself. |
| Hardware Specification | Yes | We implement our model with Py Torch and train it on a single 1080-Ti GPU. |
| Software Dependencies | No | The paper mentions "Py Torch" as the implementation framework but does not provide a specific version number for it or any other software dependencies. |
| Experiment Setup | Yes | The learning rate is initially set to 0.1 and multiplied by 0.1 per 40 epochs. Our domain discriminator Dθd consists of a 128-D and a 2-D fully connected (FC) layers with batch normalization (BN), while the identity discriminator Iθi is a 18,530-D (i.e., the number of identities) FC layer with BN. The updating rate α in Eq. (7) is set to 0.05. The triplet loss margin in Eq. (2) is 0.3. The τ of softmax in Eq. (8) is 2 10 3. The weights of the losses in Eq. (9) are set to λ1 = 1.0, λ2 = 0.18 and λ3 = 0.05. The model is trained for 100 epochs with a batch size of 64 (each identity comes with 4 images). We enable LSE after the 4th epoch to stabilize learned representations. |