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..
RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search
Authors: Yang Bai, Min Cao, Daming Gao, Ziqiang Cao, Chen Chen, Zhenfeng Fan, Liqiang Nie, Min Zhang
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that Ra Sa outperforms existing state-of-the-art methods by 6.94%, 4.45% and 15.35% in terms of Rank@1 on CUHK-PEDES, ICFG-PEDES and RSTPReid datasets, respectively. |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, Soochow University 2Institute of Automation, Chinese Academy of Sciences 3Institute of Computing Technology, Chinese Academy of Sciences 4Harbin Institute of Technology, Shenzhen |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at: https://github.com/Flame-Chasers/Ra Sa. |
| Open Datasets | Yes | We conduct experiments on three text-based person search datasets: CUHK-PEDES [Li et al., 2017], ICFG-PEDES [Ding et al., 2021] and RSTPReid [Zhu et al., 2021]. |
| Dataset Splits | No | The paper mentions evaluating on 'test images' and discusses dataset usage, but it does not provide specific train/validation/test dataset split percentages, sample counts, or explicit citations for predefined splits in the main text. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like ALBEF, TCL, CLIP, BERT, and Distil BERT, but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | The best result is achieved at pw = 0.1. ... Ra Sa performs best at pm = 0.3. ... Empirical results show that Ra Sa performs best when they are set as 0.5. |