Call for Papers

• Jun 30th, 2025 11:59 PM HST *: Deadline for Submission. Submission Website:  OpenReview

• Jul 10th, 2025 11:59 PM HST *: Notification of Acceptance

• Aug 17th , 2025 11:59 PM HST *: Camera Ready

Note: The above deadlines apply to those who want to have their papers included in the proceedings. If you prefer not to be included into the proceedings but still want to share your work with the community, please contact the organizing committee to find out possible solutions. 

Paper Submission

To ensure the high quality of the accepted papers, all submissions will be evaluated by research and industry experts from the corresponding fields. Reviewing will be double-blind and we will accept submissions on work that is not published, currently under review, and already published. All accepted workshop papers will be published in the ICCV 2025 Workshop Proceedings by Computer Vision Foundation Open Access. The authors of all accepted papers (oral/posters) will be invited to present their work during the actual workshop event at ICCV 2025. 

Paper submission has to be in English, in pdf format. ICCV will follow CVPR and only include works longer than 4 pages and up to 8 pages not including references. For anything 4 pages and under, you can organize yourself, e.g., on arXiv or self-hosting. The paper format must follow the same guidelines as for all ICCV 2025 submissions. The author kit provides a LaTeX2e template for paper submissions. Please refer to this kit for detailed formatting instructions.

Submission Website:  OpenReview

For information about whether the workshop will be in-person, virtual, or hybrid please visit the ICCV 2025 website. 

Topics of Interest 

The 4th DataCV workshop aims to 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

• Exploring Vision-Language Models (VLMs) from a data-centric perspective

In summary, the questions related to this workshop include but are not limited to:

• Can vision datasets be analyzed on a large scale?

• How to holistically understand the visual semantics contained in a dataset?

• How to 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? 

• How to measure dataset bias and fairness?

• Can we improve training data quality through data engineering or simulation?

• How to efficiently create labelled datasets under new environments?

• How to create realistic datasets that serve our real-world application purpose?

• How can we alleviate the need for large-scale labelled datasets in deep learning?

• How to analyze model performance in environments lacking annotated data?

• How can we assess model bias and fairness in vision models from a data perspective?

• How can generated data be used to alleviate privacy concerns in computer vision tasks?

• How to better evaluate diffusion models and large language models using data-centric approaches?

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