The field of computer vision and autonomous systems is rapidly advancing, with a focus on improving the accuracy and efficiency of object detection, tracking, and perception. Recent developments have seen the integration of self-supervised learning, attention mechanisms, and graph-based methods to enhance the robustness and generalization of models. Notably, the use of self-supervised learning has enabled the development of annotation-free crack detection and out-of-distribution detection methods, which have achieved state-of-the-art performance on various benchmarks. Additionally, the application of attention mechanisms has improved the accuracy of object detection and tracking, particularly in scenarios with occlusions and complex backgrounds. The development of analytical frameworks for autonomous vehicle perception has also shown promise in enhancing the safety and reliability of smart mobility systems. Some noteworthy papers in this regard include: Fast Self-Supervised depth and mask aware Association for Multi-Object Tracking, which proposes a novel method for multi-object tracking using self-supervised learning and attention mechanisms. MatchAttention: Matching the Relative Positions for High-Resolution Cross-View Matching, which introduces a new attention mechanism for cross-view matching and achieves state-of-the-art performance on several benchmarks.