Discriminative Spatial-Semantic VOS Solution: 1st Place Solution for 6th LSVOS
This work addresses segmentation challenges in complex and long-term motion scenarios for computer vision applications, representing an incremental improvement.
The paper tackled video object segmentation in complex scenes and prolonged object motions by introducing a discriminative spatial-temporal model, achieving first place with 80.90% J&F score on the 6th LSVOS challenge test set.
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}.