Graph Consistency Based Mean-Teaching for Unsupervised Domain Adaptive Person Re-Identification
Authors: Xiaobin Liu, Shiliang Zhang
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three datasets, i.e., Market-1501, Duke MTMCre ID, and MSMT17, show that proposed GCMT outperforms state-of-the-art methods by clear margin. |
| Researcher Affiliation | Academia | Xiaobin Liu, Shiliang Zhang Department of Computer Science, School of EECS, Peking University {xbliu.vmc, slzhang.jdl}@pku.edu.cn |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/liu-xb/GCMT . |
| Open Datasets | Yes | Experiments are performed on three datasets, i.e., Duke MTMC-re ID [Zheng et al., 2017], Market-1501 [Zheng et al., 2015], and MSMT17 [Wei et al., 2018]. |
| Dataset Splits | Yes | Duke MTMC-re ID contains 36,411 images of 1,812 identities. 16,522 images of 702 identities are used for training. In the rest of images, 3,368 images are selected as query images and remaining 19,732 images are used as gallery images. Market-1501 contains 32,668 images of 1,501 identities. 12,936 images of 751 identities are selected for training. In the rest of images, 3,368 images are selected as query images and remaining 19,732 images are used as gallery images. MSMT17 contains 126,441 images of 4,101 identities. 32,621 images of 1,041 identities are selected for training. In the rest of images, 11,659 images are selected as query images and remaining 82,161 images are used as gallery images. |
| Hardware Specification | Yes | Models are trained on a server with three Ge Force RTX 2080 Ti GPUs and one Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' and 'K-Means method' but does not specify any software or library names with version numbers. |
| Experiment Setup | Yes | Input images are resized to 256 128. We use random flipping, random cropping, and random erasing [Zhong et al., 2020] for data augmentation. K-Means method is used for unsupervised clustering. The number of clusters is set to 500 on Duke and Market and 1,500 on MSMT following [Ge et al., 2020a; Zhai et al., 2020]. In each training batch, 16 identities are randomly selected and 4 images for each identity are selected, resulting 64 images. K is set as 12 in teacher graph construction. Loss weight λGCC is set as 0.6. β is set to 0.05 following [Liu and Zhang, 2020]. Adam optimizer is used for training. Learning rate is initialized as 0.00035 and decayed by 0.1 after 20 epochs. Models are totally trained for 120 epochs with 400 iterations in each epoch. |