Multi-Centroid Representation Network for Domain Adaptive Person Re-ID
Authors: Yuhang Wu, Tengteng Huang, Haotian Yao, Chi Zhang, Yuanjie Shao, Chuchu Han, Changxin Gao, Nong Sang2750-2758
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superiority of MCRN over state-of-the-art approaches on multiple UDA re-ID tasks and fully unsupervised re-ID tasks. Sections like "Experiments", "Ablation Studies", and "Comparison with the State-of-the-Arts" include performance tables (Table 1, 5, 6) showing empirical results. |
| Researcher Affiliation | Collaboration | 1Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology 2Megvii Technology |
| Pseudocode | No | The paper describes mechanisms and mathematical formulations, but it does not include a dedicated "Pseudocode" or "Algorithm" block, figure, or section with structured code-like steps. |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing its source code for the methodology described, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | We evaluate our method on three person re-ID datasets, including Market-1501 (Zheng et al. 2015), Duke MTMC-re ID (Ristani et al. 2016) and MSMT17 (Wei et al. 2018). |
| Dataset Splits | No | The paper mentions evaluating on different datasets (source and target domains for UDA re-ID) but does not provide explicit numerical training, validation, and test dataset splits (e.g., percentages or counts) for reproducibility of data partitioning. It describes mini-batch sampling but not overall dataset splits. |
| Hardware Specification | Yes | We implement our approach using the Pytorch (Paszke et al. 2019) framework and use four NVIDIA RTX-2080TI GPUs for training. |
| Software Dependencies | No | The paper mentions "Pytorch (Paszke et al. 2019) framework" but does not specify a version number for PyTorch or any other software dependencies like CUDA, Python, or specific libraries with their versions. |
| Experiment Setup | Yes | Each mini-batch consists of 64 source domain images and 64 target domain images, with 4 images per ground-truth/pseudo class (i.e., K is set to 4). All training images are resized to 256 × 128 and various data augmentations are applied, including random cropping, random flipping and random erasing (Zhong et al. 2020). Adam optimizer is utilized to optimize the encoder with a weight decay of 0.0005. The initial learning rate is set to 0.00035 and is decayed by 1/10 every 20 epochs in the total 50 epochs. The momentum coefficient m in Equation 2 is set to 0.2, and the temperature coefficient τ in the contrastive losses is set to 0.05. α in SONI is set to 0.03. |