CLVision Challenge

The CLVision Challenge focuses on advancing continual learning (CL) in computer vision, particularly addressing the challenges of class incremental learning with recurring classes and the presence of unlabeled or out-of-distribution data. Current research emphasizes strategies like ensemble methods, winning subnetworks, and teacher-student models incorporating techniques such as contrastive learning and pseudo-labeling to mitigate catastrophic forgetting and improve performance on imbalanced datasets. These advancements are significant for building robust and adaptable vision systems capable of handling real-world data streams characterized by evolving object categories and noisy information, impacting applications like autonomous driving and video object segmentation.