Data-Centric Computer Vision for Image Processing

Dear Colleagues,

Data are the fuel of computer vision, being the basis on which state-of-the-art systems are built. A robust object detection system not only needs a strong model architecture and learning algorithms but also relies on a comprehensive large-scale training set. Despite the pivotal significance of datasets, existing research in computer vision is usually algorithm-centric. Comparing the number of algorithm-centric works in domain adaptation, our quantitative understanding of the domain gap is much more limited. As a result, there are currently few investigations into the representations of datasets, while in contrast, there is an abundance of literature regarding the best ways to represent images or videos, essential elements in datasets. 

Our Special Issue will bring together research works and discussions focusing on analyzing vision datasets, as opposed to the commonly seen algorithm-centric counterparts. Specifically, the following topics are of interest in this workshop. 

• Properties and attributes of vision datasets;
• Application of dataset-level analysis;
• Representations of and similarities between vision datasets;
• Improving vision dataset quality through generation and simulation;
• Evaluating model accuracy under various test environments. 

In summary, the questions related to the proposed Special Issue include but are not limited to the following:

• Can vision datasets be analyzed on a large scale?
• How can we holistically understand the visual semantics contained in a dataset?
• How can we define vision-related properties and problems on the dataset level?
• How can we improve algorithm design by better understanding vision datasets?
• Can we predict the performance of an existing model in a new dataset?
• What are good dataset representations? Can they be hand-crafted, learned through neural nets, or a combination of both?
• How do we measure similarities between datasets and their bias and fairness?
• Can we improve training data quality through data engineering or simulation?
• How can we efficiently create labeled datasets under new environments?
• How can we create realistic datasets that serve our real-world application purpose
• How can we alleviate the need for large-scale labeled datasets in deep learning?
• How can we best analyze model performance under various environments without accessing the ground truth labels?
• How can we evaluate diffusion models and large language models?

Dr. Yue Yao
Prof. Dr. Tom Gedeon
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• data-centric AI
• dataset presentation
• synthetic data
• data augmentation
• domain adaptation