[ML-news] [1st CFP] ECCV2024 - International Workshop on Synthetic Data for Computer Vision (SyntheticData4CV 2024)
Lucia CASCONE
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Call for Papers
Workshop on Synthetic Data for Computer Vision (SyntheticData4CV 2024)
https://syntheticdata4cv.wordpress.com/
to be held as part of the 18th European Conference on Computer Vision (ECCV 2024)
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Important Dates
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Submission Deadline: July 10, 2024
Notification of Acceptance: August 7, 2024
Camera Ready Deadline: August 25, 2024
Workshop: Sept 29 or Sept 30, 2024 (to be defined) - Milan, Italy
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Workshop Aims and Scope
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In the dynamic landscape of computer vision, synthetic data is emerging as a key resource to overcome data limitations and improve the accuracy and reliability of systems. Advanced generative models like diffusion models, GANs, and multimodal models can generate large amounts of data with different characteristics to address data insufficiency and bias, as well as privacy concerns, by removing sensitive information or generating data without it.
These advances are particularly significant in several areas, such as healthcare, where privacy and data protection laws limit access to real patient data. In addition, simulating rare medical disorders could improve model accuracy and generalizability. Furthermore, these data present a compelling opportunity for resource optimization by removing the need to store massive datasets; they can be generated and dynamically provided to learning models during training. This workshop aims to explore not only the favorable impacts of synthetic data, such as those just delineated, but also the challenges and risks associated with their potential misuse. Particularly in the area of security, synthetic data could be exploited to circumvent biometric recognition systems, undermining their effectiveness and enabling unauthorized access or fraudulent activities. Therefore, there is an imperative not only to generate increasingly high-quality data but also to develop robust algorithms for their detection.
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Workshop Keywords
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Image Synthesis; Responsible Synthetic Data Use; Computer Vision; Synthetic Biometrics; Synthetic dataset; Medical image synthesis
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Workshop Topics
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The workshop welcomes contributions on all topics related to synthetic data in the field of computer vision, focused (but not limited to):
Responsible Image Synthesis:
Fairness & Robustness: For example, collecting and preparing synthetic datasets that fairly represent different classes; simulating the most difficult and rare conditions to improve the robustness and generalizability of systems.
Bias & Ethics: For instance, implementing validation procedures to ensure that synthetic datasets do not contain unintentional bias; setting clear ethical guidelines to prevent the misuse of that data.
Privacy & Security: Examples include anonymization and de-identification to prevent data linking to real people; using synthetic data to enhance communication rounds in federated learning.
Reliability: Conducting experiments to test reliability and accuracy in various application contexts by comparing the results achieved using synthetic information with those obtained using real data; enabling a deeper understanding of the algorithms and decisions made in the generation process.
Generative Models:
Stable Diffusion Models: Such as personalization, conditional generation, guidance, and controllability; innovative approaches for training or architectures.
3D Models: Overcoming challenges related to shape diversity, structure, and object complexity; exploring how they can be integrated into VR and AR applications.
Deepfakes: Developing algorithms and systems to identify and neutralize deepfake content to prevent misinformation and protect individual identities; forensic techniques for analysis and attribution of deepfake videos.
Learning from Synthetic Data:
Frameworks: Domain generalization by training models on a wide array of simulated conditions; addressing the domain adaptation challenge, and assessing the effectiveness of transfer learning techniques.
Strategies: Continual learning by generating dynamic datasets that reflect evolving conditions and novel challenges; one- or few-shot learning by generating diverse and comprehensive datasets from a limited number of real-world examples.
Theoretical Foundations: Development of benchmarks and validation protocols to evaluate the effectiveness of models trained on synthetic data; establishing standardized tests to ensure reliability and robustness across various synthetic datasets.
Applications:
Medical Image Synthesis:
Generation of Synthetic Diagnostic Images: Improving the realism, diversity, and clinical relevance of synthetic medical images to aid in training and evaluating diagnostic systems; ethical implications of using synthetic data in healthcare: patient privacy concerns, and strategies to mitigate potential risks.
Simulation of Pathological Variants for Medical Model Training: Incorporating expert knowledge into a machine learning model to define a fine-tuning objective; techniques for tailoring synthetic images conditioned on clinical concepts.
Synthetic Data for Disease Progression Modeling: Frameworks that capture the temporal evolution of diseases; simulations of the impact of potential treatments and interventions, providing a controlled environment for testing novel medical strategies.
Synthetic Biometrics:
Innovative Synthesis of Biometric Data: Development of advanced techniques for synthetic biometric generation; identification of core technical challenges.
Label Generation: Implementation of automatic annotation techniques; ensuring label accuracy and consistency.
Quality Assessment: Methods to evaluate synthetic biometric data quality and realism; comparison tools and metrics against real data.
Others.
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Submission Details
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Format and paper length
All submissions must be presented in English. Authors are encouraged to refer to the ECCV 2024 Suggested Practices for Authors and utilize the official proceedings templates provided for either LaTeX or Word formats.
We invite participants to submit their contributions to the workshop in the form of full papers. These submissions may extend up to 14 pages, inclusive of figures and tables, formatted according to the Springer LNCS style. Additional pages dedicated solely to cited references are permissible.
Submission policy
Submissions must contain original research that has not been previously published. Authors are encouraged to adhere to the ECCV paper preparation guidelines. All papers must be submitted electronically through the CMT system.
Double-Blind Review Policy
Submissions will undergo a double-blind review process. To ensure anonymity:
Remove all identifying information, including author names and institutional affiliations, from the title and header areas of the paper.
Omit acknowledgments.
Maintain citations to your own previous work unanonymized to allow reviewers to assess all relevant research, but refer to your work in the third person (e.g., "[22] found that...").
Papers that are not properly anonymized, do not follow the provided template, or fail to comply with these guidelines will be rejected without review. During the review, authors can address the reviewers' comments in a rebuttal period before a final decision is made. Each submission will be reviewed by at least three members of the Program Committee. Authors are encouraged to make their code and data available anonymously (e.g., through an anonymous GitHub repository). Supplementary materials like images, videos, appendices, and technical reports can optionally be included, but must also be anonymized.
Proceedings
Accepted papers will be published as part of the official ECCV 2024 workshop proceedings. Furthermore, the best papers will be invited to submit an extended version to the Elsevier Image and Vision Computing Journal, which has an impact factor of 4.7. This opportunity allows for additional visibility and an extended presentation of their work. These extended manuscripts will undergo another peer-review process. The authors must submit an extended version of the accepted paper with at least 30 or 40% new and original content.
Author Guidelines for Workshop Participation
Papers not selected for oral presentation will be presented in a poster session. We expect each paper to be presented in-person by an author (or an authorized delegate).
Note: publication of the paper in the ECCV 2024 proceedings of Springer requires that at least one of the authors registers for the conference.
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Workshop Organizers
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Lucia Cascone
University of Salerno, Fisciano, Italy
Email: lcas...@unisa.it
Zilong Huang
Tencent, China
Email: zilong.h...@gmail.com
Michele Nappi
University of Salerno, Fisciano, Italy
Email: mna...@unisa.it
Xinggang Wang
Huazhong University of Science and Technology, China
Email: xgw...@hust.edu.cn
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Contacts
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For general inquiries regarding the workshop, please direct your emails to: syntheti...@gmail.com