The 1st Solution for MOSEv1 Challenge on LSVOS 2025: CGFSeg

• URL: http://arxiv.org/abs/2509.25738v1
• Date: Tue, 30 Sep 2025 03:50:56 GMT
• Title: The 1st Solution for MOSEv1 Challenge on LSVOS 2025: CGFSeg
• Authors: Tingmin Li, Yixuan Li, Yang Yang,
• Abstract summary: Video Object (VOS) aims to track and segment specific objects across entire video sequences.<n>In this paper, we present our improved method, Confidence-Guided Fusion extraction (CGFSeg) for the VOS task in the MOSEv1 Challenge.<n>Our method achieves a J&F score of 86.37% on the test set, ranking 1st in the MOSEv1 Challenge at LSVOS 2025.
• Score: 19.13013862040698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video Object Segmentation (VOS) aims to track and segment specific objects across entire video sequences, yet it remains highly challenging under complex real-world scenarios. The MOSEv1 and LVOS dataset, adopted in the MOSEv1 challenge on LSVOS 2025, which is specifically designed to enhance the robustness of VOS models in complex real-world scenarios, including long-term object disappearances and reappearances, as well as the presence of small and inconspicuous objects. In this paper, we present our improved method, Confidence-Guided Fusion Segmentation (CGFSeg), for the VOS task in the MOSEv1 Challenge. During training, the feature extractor of SAM2 is frozen, while the remaining components are fine-tuned to preserve strong feature extraction ability and improve segmentation accuracy. In the inference stage, we introduce a pixel-check strategy that progressively refines predictions by exploiting complementary strengths of multiple models, thereby yielding robust final masks. As a result, our method achieves a J&F score of 86.37% on the test set, ranking 1st in the MOSEv1 Challenge at LSVOS 2025. These results highlight the effectiveness of our approach in addressing the challenges of VOS task in complex scenarios.

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