The recent advancements in the field of computer vision have seen a significant shift towards more efficient and versatile models, particularly in the context of object segmentation, tracking, and person re-identification. Vision Transformers (ViTs) have been a focal point, with innovations aimed at reducing computational complexity while maintaining or even enhancing performance. Lightweight ViTs, such as those proposed in EfficientTAMs, have shown promising results in video object segmentation and tracking, offering substantial speedups and parameter reductions without compromising quality. Additionally, the integration of multi-view normalization and token mixing in models like MVFormer has diversified feature learning, leading to superior performance across various vision tasks. Dynamic token selection and semantic contextual integration have also been explored to address specific challenges in person re-identification, particularly in scenarios involving viewpoint discrepancies and cloth-changing conditions. These developments collectively indicate a trend towards more adaptable and efficient models that can handle a wide range of vision tasks with greater precision and reduced computational overhead.