The recent advancements in the research area are primarily focused on enhancing model generalization and addressing long-tailed distributions in datasets. A significant trend is the development of novel optimization techniques and loss functions designed to guide models towards flatter minima in the loss landscape, thereby improving their ability to generalize across different domains. These methods often integrate multi-scale feature fusion and adaptive loss balancing to handle the variability and sparsity in labeled data, particularly in outdoor and multimodal datasets. Additionally, there is a growing emphasis on self-supervised learning and sampling strategies to manage extreme class imbalances and domain disparities. Notably, the integration of meta-learning and curvature-aware minimization is emerging as a powerful approach for domain generalization, with theoretical backing and empirical validation on benchmark datasets. These innovations collectively push the boundaries of current methodologies, offering more robust and versatile solutions for complex classification tasks.