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..
Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification
Authors: Lan Wei, Yonghong Tian, Yaowei Wang, Tiejun Huang
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three indoor and outdoor public datasets demonstrate that our model outperforms several state-of-the-art methods remarkably. |
| Researcher Affiliation | Academia | 1School of EE & CS, Peking University, Beijing 100871, China 2Department of Electronic Engineering, Beijing Institute of Technology, Beijing 100081, China |
| Pseudocode | Yes | Algorithm 1 Swiss-system based cascade ranking algorithm |
| Open Source Code | No | The paper does not provide any statements or links regarding the release of the source code for the described methodology. |
| Open Datasets | Yes | Extensive experiments have been carried out on the three gait databases: CASIA, Soton and PKU Human ID. As shown in Fig. 4, they cover an indoor environment (CASIA), outdoor (Soton) and no controlled scenario (PKU). |
| Dataset Splits | No | The paper mentions dividing subjects into training and testing sets but does not specify a separate validation set or describe how validation was performed. |
| Hardware Specification | No | The paper does not mention specific hardware components (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., library names like PyTorch, TensorFlow with their versions). |
| Experiment Setup | No | The paper describes the experimental settings (e.g., datasets, splits, comparison methods), but it does not provide concrete hyperparameter values or detailed system-level training configurations (e.g., learning rate, batch size, optimizer details). |