  Video Object Segmentation (VOS) is a vital task in computer vision, focusing
on distinguishing foreground objects from the background across video frames.
Our work draws inspiration from the Cutie model, and we investigate the effects
of object memory, the total number of memory frames, and input resolution on
segmentation performance. This report validates the effectiveness of our
inference method on the coMplex video Object SEgmentation (MOSE) dataset, which
features complex occlusions. Our experimental results demonstrate that our
approach achieves a J\&F score of 0.8139 on the test set, securing the third
position in the final ranking. These findings highlight the robustness and
accuracy of our method in handling challenging VOS scenarios.
