Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Metric Embedded Discriminative Vocabulary Learning for High-Level Person Representation
Authors: Yang Yang, Zhen Lei, Shifeng Zhang, Hailin Shi, Stan Li
AAAI 2016 | Venue PDF | 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 EMAIL |
| 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. |