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.

视频目标分割是计算机视觉中的重要任务，研究了切割模型的灵感、对象记忆、内存帧总数和输入分辨率对分割性能的影响，并在复杂视频目标分割（MOSE）数据集上验证了我们方法的有效性，实验结果表明在测试集上达到了 0.8139 的 J&F 得分，在处理具有挑战性的 VOS 场景中具有强大的鲁棒性和准确性。