The field of video object segmentation is moving towards more robust and generalizable models, with a focus on unsupervised and autoregressive approaches. Recent developments have shown promising results in addressing challenges such as temporal inconsistencies, deformation, and fast motion. Noteworthy papers include FTIO, which achieves state-of-the-art performance in multi-object unsupervised video object segmentation, and AUSM, which unifies prompted and unprompted video segmentation in a single architecture. Other notable papers include FreeVPS, which repurposes SAM2 for generalizable video polyp segmentation, and AutoQ-VIS, which improves unsupervised video instance segmentation via automatic quality assessment. These advancements have the potential to improve the accuracy and efficiency of video object segmentation in various applications.