Multi-Channel Pyramid Person Matching Network for Person Re-Identification
Authors: Chaojie Mao, Yingming Li, Yaqing Zhang, Zhongfei Zhang, Xi Li
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on several benchmark datasets demonstrate the effectiveness of our approach against the state-of-the-art literature, especially on the rank-1 recognition rate. |
| Researcher Affiliation | Collaboration | 1 College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, China 2 College of Computer Science and Technology, Zhejiang University, Hangzhou, China 3 Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Hangzhou, China |
| Pseudocode | No | The paper includes network architecture diagrams (e.g., Figure 2) and descriptive text, but no formally structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We evaluate the proposed architecture and compare our results with those of the state-of-the-art approaches on six person Re-ID datasets, namely CUHK03 (Li et al. 2014), CUHK01 (Li, Zhao, and Wang 2012), VIPe R (Gray and Tao 2008), PRID450s (Roth et al. 2014), i-LIDS (Office 2008) and PRID2011 (Hirzer et al. 2011). ... Table lists the description of each dataset and our experimental settings with the training and testing splits. |
| Dataset Splits | Yes | Table lists the description of each dataset and our experimental settings with the training and testing splits. ... For the CUHK01 dataset, we report results on two different settings: 100 test IDs, and 486 test IDs. |
| Hardware Specification | Yes | The proposed architecture is implemented on the widely used deep learning framework Caffe (Jia et al. 2014) with an NVIDIA TITAN X GPU. |
| Software Dependencies | No | The paper mentions using "Caffe (Jia et al. 2014)" but does not provide specific version numbers for Caffe or any other software dependencies. |
| Experiment Setup | Yes | The parameters for training SC-PPMN, CTM-PPMN and MCPPMN are listed in Table 2. ... Table 2: The parameters for training. Parameters: Maximum Iteration, Batch Size, Momentum, Weight Decay, Base Learning Rate. |