Metric Embedded Discriminative Vocabulary Learning for High-Level Person Representation
Authors: Yang Yang, Zhen Lei, Shifeng Zhang, Hailin Shi, Stan Li
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on person re-identification demonstrate the effectiveness of our proposed algorithm. |
| Researcher Affiliation | Academia | Yang Yang, Zhen Lei, Shifeng Zhang, Hailin Shi, Stan Z. Li Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China {yang.yang, zlei, shifeng.zhang, hailin.shi, szli}@nlpr.ia.ac.cn |
| Pseudocode | Yes | Algorithm 1 Metric Embedded Discriminative Vocabulary Learning |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | VIPe R dataset (Gray, Brennan, and Tao 2007), PRID 450S dataset (Roth et al. 2014) |
| Dataset Splits | No | In all experiments, half image pairs are randomly selected for training and the remaining are employed for test. The paper specifies train and test splits, but does not explicitly mention a separate validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details (like GPU/CPU models or memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions general software or tools but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | In our evaluations, we set α and β in Eq 8 to 0.5 and 0.2, respectively. The number of basis vectors in B is set to 120 and the iteration number T to 4. Before using KISSME, we employ PCA to reduce the 120-dimensional high-level features to 70 for both datasets. When SAC is compared, we set γ to 0.05. |