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 [1].
The State of Computer Vision Research in Africa
Authors: Abdul-Hakeem Omotayo, Ashery Mbilinyi, Lukman Ismaila, Houcemeddine Turki, Mahmoud Abdien, Karim Gamal, Idriss Tondji, Yvan Pimi, Naome A. Etori, Marwa M. Matar, Clifford Broni-Bediako, Abigail Oppong, Mai Gamal, Eman Ehab, Gbetondji Dovonon, Zainab Akinjobi, Daniel Ajisafe, Oluwabukola G. Adegboro, Mennatullah Siam
JAIR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This study analyzes 63,000 Scopus-indexed computer vision publications from Africa. We utilize large language models to automatically parse their abstracts, to identify and categorize topics and datasets. This resulted in listing more than 100 African datasets. Our objective is to provide a comprehensive taxonomy of dataset categories to facilitate better understanding and utilization of these resources. We also analyze collaboration trends of researchers within and outside the continent. Additionally, we conduct a large-scale questionnaire among African computer vision researchers to identify the structural barriers they believe require urgent attention. |
| Researcher Affiliation | Academia | Abdul-Hakeem Omotayo EMAIL University of California, Davis, USA; Ashery Mbilinyi EMAIL University of British Columbia, Canada; Lukman E. Ismaila EMAIL Johns Hopkins University, USA; Houcemeddine Turki EMAIL University of Sfax, Tunisia; Mahmoud Abdien, Karim Gamal 21mmah,EMAIL Queen s University, Canada; Idriss Tondji, Yvan Pimi itondji,EMAIL African Institute for Mathematical Sciences, Senegal; Naome A. Etori EMAIL University of Minnesota-Twin Cities, USA; Marwa M. Matar EMAIL Al-Azhar University in Cairo, Egypt; Clifford Broni-Bediako EMAIL RIKEN Center for Advanced Intelligence Project, Japan; Abigail Oppong EMAIL Ashesi University, Ghana; Mai Gamal EMAIL German University in Cairo, Egypt; Eman Ehab EMAIL Nile University, Egypt; Gbetondji Dovonon EMAIL University College London, UK; Zainab Akinjobi EMAIL University of California, Davis, USA; Daniel Ajisafe EMAIL University of British Columbia, Canada; Oluwabukola G. Adegboro EMAIL Dublin City University, Ireland; Mennatullah Siam EMAIL Ontario Tech University & University of British Columbia, Canada |
| Pseudocode | No | The paper describes its methodology through narrative text and a pipeline diagram (Figure 1), but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We gather and show the aforementioned examples of publications that were rejected during our verification phase in our code repository 8. https://github.com/Ro-ya-cv4Africa/acvsurvey/ |
| Open Datasets | Yes | For the full collection of African computer vision datasets refer to our publicly available dataset repository 30. https://github.com/Ro-ya-cv4Africa/acvdatasets |
| Dataset Splits | No | This paper is a survey and analysis of computer vision research and datasets in Africa; it does not describe experimental methodology that would involve training, validation, and test dataset splits for machine learning models. |
| Hardware Specification | No | The paper mentions utilizing large language models and Scopus APIs for its analysis but does not provide any specific hardware details such as GPU/CPU models, memory, or cloud computing specifications used for its experiments. |
| Software Dependencies | No | The paper mentions tools such as 'large language models (i.e., GPT series)', 'Scopus APIs', 'Sci Val', 'pybliometrics', and 'scholarly third party libraries' but does not specify version numbers for any of these software dependencies. |
| Experiment Setup | No | This paper conducts a survey and analysis of computer vision research; it does not describe the training or evaluation of machine learning models and therefore does not specify experimental setup details such as hyperparameters or training configurations. |