Discriminative Spatial-Semantic VOS Solution: 1st Place Solution for 6th LSVOS

TL;DR

This paper presents a discriminative spatial-temporal VOS model that leverages semantic understanding and salient features, achieving first place in the 6th LSVOS challenge and advancing the handling of complex scenes and long-term object motions.

Contribution

It introduces a novel discriminative spatial-temporal VOS approach utilizing semantic modules and salient features, outperforming previous methods in challenging scenarios.

Findings

Achieved 80.90% J&F score on 6th LSVOS test set.

Outperformed existing methods in complex scenes and long-term object motion.

Code available for reproducibility and further research.

Abstract

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}.