Self-Supervised Video Object Segmentation for Autonomous Vehicles: A Framework for Processing Unlabeled Data and Foreground-Background Discriminatio

Challenges faced: High dimensionality in video frames poses challenges in feature extraction and representation. Uncertainty in SSL predictions makes it challenging to create robust classifiers. Inadequacy in capturing the diversity of hidden aspects through single predictions. The difficulty in finding suitable incompatible pairs for contrastive learning, particularly in high-dimensional spaces.

Learning outcomes: Enhanced understanding of Self-Supervised Learning (SSL) as a versatile paradigm for AI model development. Awareness of the potential of SSL in complex computer vision tasks. Insight into various SSL challenges and strategies to address them, including energy-based models, joint embeddings, contrastive learning, and latent variable predictive models. Recognition of the need for non-contrastive methods to improve SSL efficiency.