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..
An Empirical Study of CLIP for Text-Based Person Search
Authors: Min Cao, Yang Bai, Ziyin Zeng, Mang Ye, Min Zhang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper makes the first attempt to conduct a comprehensive empirical study of CLIP for TBPS and thus contribute a straightforward, incremental, yet strong TBPS-CLIP baseline to the TBPS community. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Technology, Soochow University 2 School of Computer Science, Wuhan University 3 Harbin Institute of Technology, Shenzhen |
| Pseudocode | No | The paper describes methods and equations but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/Flame-Chasers/TBPS-CLIP. |
| Open Datasets | Yes | Comparisons with other methods are carried out on three datasets: CUHK-PEDES (Li et al. 2017b), ICFG-PEDES (Ding et al. 2021), RSTPReid (Zhu et al. 2021). |
| Dataset Splits | No | The paper mentions "few-shot capabilities (5% training data)" but does not explicitly state the full training/validation/test dataset splits for its main experiments. Details are deferred to the Appendix, which is not part of the main text analysis. |
| Hardware Specification | No | No specific hardware details (GPU models, CPU types, or cloud platforms) are mentioned for the experiments. |
| Software Dependencies | No | The paper mentions "Py Torch" but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The paper discusses "Training Tricks" such as global gradients back-propagation, dropout, locking bottom layers, and soft label. It also mentions hyperparameters like "τs is a hyper-parameter and set to 0.1" and "training in just 5 epochs". |