Semantics-Aligned Representation Learning for Person Re-Identification
Authors: Xin Jin, Cuiling Lan, Wenjun Zeng, Guoqiang Wei, Zhibo Chen11173-11180
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Ablation studies demonstrate the effectiveness of our design. We achieve the state-of-the-art performances on the benchmark datasets CUHK03, Market1501, MSMT17, and the partial person re ID dataset Partial REID. |
| Researcher Affiliation | Collaboration | Xin Jin,1 Cuiling Lan,2 Wenjun Zeng,2 Guoqiang Wei,1 Zhibo Chen1 University of Science and Technology of China1 Microsoft Research Asia2 {jinxustc, wgq7441}@mail.ustc.edu.cn, {culan, wezeng}@microsoft.com, chenzhibo@ustc.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link for the release of source code for the described methodology. |
| Open Datasets | Yes | We conduct experiments on six benchmark person re ID datasets, including CUHK03 (Li et al. 2014), Market1501 (Zheng, Shen, and others 2015), Duke MTMC-re ID (Zheng, Zheng, and Yang 2017), the large-scale MSMT17 (Wei, Zhang, and others 2018), and two challenging partial person re ID datasets of Partial REID (Zheng et al. 2015) and Partial-i LIDS (He et al. 2018). |
| Dataset Splits | No | The paper mentions using benchmark datasets and following "common practices" but does not explicitly provide specific train/validation/test dataset splits with percentages, sample counts, or direct references to how these splits were configured. |
| Hardware Specification | No | The paper only mentions "a single GPU" for training without specifying the exact model or any other specific hardware components (CPU, RAM, etc.) used for experiments. |
| Software Dependencies | No | The paper mentions using ResNet-50 but does not provide specific software dependencies or library versions (e.g., Python, PyTorch/TensorFlow, CUDA versions) needed to replicate the experiment. |
| Experiment Setup | Yes | For a batch of re ID data, we experimentally set λ1 to λ4 as 0.5, 1.5, 1, 1. For a batch of synthesized data, λ1 to λ4 are set to 0, 0, 1, 0 where the re ID losses and Triplet Re ID constraints (losses) are not used. The margin parameter m is set to 0.3 experimentally. |