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..
Learning Cross-View Binary Identities for Fast Person Re-Identification
Authors: Feng Zheng, Ling Shao
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on two public datasets and CBI produces comparable results as state-of-the-art re-identification approaches but is at least 2200 times faster. |
| Researcher Affiliation | Academia | 1Department of Electronic and Electrical Engineering, The University of Sheffield. 2Department of Computer Science and Digital Technologies, Northumbria University. |
| Pseudocode | Yes | Algorithm 1 CBI training |
| Open Source Code | Yes | The codes are released on a website: https://sites.google.com/site/crossmodalhashing/re-identification |
| Open Datasets | Yes | We test our proposed CBI for person re-identification on two public datasets: VIPe R [Gray and Tao, 2008] and CUHK01 [Li et al., 2014]. |
| Dataset Splits | Yes | We randomly partition a dataset into two parts without overlap on person identities, according to a certain percentage. The expectation is reported by conducting 10 trials of evaluation. |
| Hardware Specification | Yes | All algorithms are run on a Matlab 7 platform installed on Windows 7 with Intel Core 3.4GHz CPU and 8G memory. |
| Software Dependencies | Yes | All algorithms are run on a Matlab 7 platform installed on Windows 7 with Intel Core 3.4GHz CPU and 8G memory. |
| Experiment Setup | Yes | CBI is not sensitive to the parameters for the two datasets and we set λ1 = 2 and C = 200 for all the experiments. However, λ2 will be set to 0.05, 10 and 5 for ELF, SCNCD and LOMO, respectively. |