Overview
The First Workshop on Statistical Deep Learning in Computer Vision (SDL-CV) is inviting submissions for extended abstracts for both oral and poster presentations. This event will take place in conjunction with the International Conference on Computer Vision (ICCV) 2019, providing a platform for researchers to discuss advancements in statistical approaches to deep learning within the computer vision domain.
Background & Relevance
Deep learning has emerged as a pivotal technique in computer vision, enabling significant advancements in tasks such as object detection, segmentation, and image enhancement. Understanding the statistical foundations of these models is crucial for improving their performance and applicability. This workshop aims to bridge the gap between theoretical insights and practical implementations, fostering collaboration among researchers and practitioners in the field.
Key Details
- Submission Deadline (Extended): August 8, 2019
- Author Notification: September 4, 2019
- Camera-Ready Deadline: September 25, 2019
- Location: In conjunction with ICCV 2019
- Workshop Website: sdlcv-workshop.com
- Topics Covered: Statistical understanding of deep learning, uncertainty in deep learning, probabilistic deep learning, and more.
Eligibility & Participation
This workshop is targeted at graduate students, researchers, and practitioners who are engaged in developing novel statistical deep learning algorithms or applying them to real-world computer vision challenges. Participants are encouraged to submit original contributions that align with the workshop’s themes.
Submission or Application Guidelines
- Submissions should be in English and formatted as a PDF.
- The length must not exceed four pages, excluding references.
- Follow the ICCV 2019 submission guidelines for formatting.
- Accepted papers will be presented in oral and poster sessions and may be invited for further publication in a special issue of the International Journal of Computer Vision (IJCV) or as book chapters.
Additional Context / Real-World Relevance
Statistical deep learning is vital for enhancing the reliability and interpretability of AI systems in computer vision. By focusing on statistical methods, this workshop addresses critical challenges in the field, such as model uncertainty and optimization, which are essential for deploying AI solutions in practical applications like autonomous driving and medical imaging.
Conclusion
The SDL-CV workshop presents a unique opportunity for researchers to share their findings and engage with peers in the rapidly evolving field of statistical deep learning. Interested parties are encouraged to submit their work and participate in this collaborative environment to advance the understanding and application of deep learning in computer vision.
Category: CFP & Deadlines
Tags: deep learning, computer vision, statistical methods, ICCV, probabilistic models, machine learning, object recognition, image enhancement
