The workshop is expected to attract research on self-supervised, semi-supervised and self-training techniques for achieving industry-level autonomous driving solutions, which will cover but are not limited to the following topics:
- Self-supervised learning techniques
- Life-long/incremental visual recognition methods
- Weakly supervised learning algorithms
- One/few/zero shot learning for perception tasks in self-driving
- Learning in the presence of noisy data
- Domain adaptation
- Weakly supervised learning for 3D Lidar and 2D images
- Real world self-driving image applications, e.g. lane detection, anomaly detection, object semantic segmentation/detection/localization, scene parsing, etc.
- Vision-based localization and tracking
- Safety/explainability/robustness for self-driving cars in the abovementioned settings
We invite submissions of full papers, as well as works-in-progress, position papers, and papers describing open problems and challenges. While original contributions are preferred, we also invite submissions of high-quality work that has recently been published in other venues or is concurrently submitted. Papers should be up to 4 pages in length (excluding references) formatted using the ICCV template. All the submissions should be anonymous. An optional appendix can be added in the submission, after references. There is no page limit for appendix. The accepted papers are allowed to get submitted to other conference venues. This workshop has no archival proceedings.
Papers can be submitted through CMT https://cmt3.research.microsoft.com/SSLAD2021
Paper submission deadline: August 22, 2021 (11:59PM Pacific Time)
Author notification: September 12, 2021 (11:59PM Pacific Time)
Camera-ready papers due: September 26, 2021 (11:59PM Pacific Time)