Video-Based Person Re-Identification via Self Paced Weighting
Authors: Wenjun Huang, Chao Liang, Yi Yu, Zheng Wang, Weijian Ruan, Ruimin Hu
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two public datasets demonstrate the superiority of the proposed method over current state-of-the-art work. |
| Researcher Affiliation | Academia | 1State Key Laboratory of Software Engineering, Wuhan University, China 2Collaborative Innovation Center of Geospatial Technology, China 3National Engineering Research Center for Multimedia Software, Wuhan University, China 4Digital Content and Media Sciences Research Division, National Institute of Informatics, Japan |
| Pseudocode | No | The paper describes solution steps in prose but does not provide structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | Our experiments are conducted on two publicly available video datasets for video-based person re-id: the PRID 2011 dataset (Hirzer et al. 2011) and the i LIDS-VID dataset (Wang et al. 2014). |
| Dataset Splits | No | In our experiments, all datasets are randomly split into two subsets, half for training and half for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions key parameters like α, β, and γ in the SPOD model, and how they are determined by cross-validation, but does not provide their specific numerical values or other concrete hyperparameter settings. |