Video object segmentation (VOS) is a crucial task in computer vision, but current VOS methods struggle with complex scenes and prolonged object motions. To address these challenges, the MOSE dataset aims to enhance object recognition and differentiation in complex environments, while the LVOS dataset focuses on segmenting objects exhibiting long-term, intricate movements. This report introduces a discriminative spatial-temporal VOS model that utilizes discriminative object features as query representations. The semantic understanding of spatial-semantic modules enables it to recognize object parts, while salient features highlight more distinctive object characteristics. Our model, trained on extensive VOS datasets, achieved first place (\textbf{80.90\%} $\mathcal{J \& F}$) on the test set of the 6th LSVOS challenge in the VOS Track, demonstrating its effectiveness in tackling the aforementioned challenges. The code will be available at \href{https://github.com/yahooo-m/VOS-Solution}{code}.

本研究针对当前视频目标分割方法在复杂场景和长时间物体运动中的不足，提出了一种区分性空间时序视频目标分割模型。该模型通过利用区分性物体特征作为查询表示，结合空间语义模块的理解，取得了第六届LSVOS挑战赛的第一名（80.90% $\mathcal{J \ F}$），展示了其在复杂环境下的有效性和精确度。

区分性空间语义视频目标分割解决方案：第六届LSVOS竞赛第一名解决方案