{"ID":2862486,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.25738","arxiv_id":"2509.25738","title":"The 1st Solution for MOSEv1 Challenge on LSVOS 2025: CGFSeg","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\u0026F 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.","short_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 mode...","url_abs":"https://arxiv.org/abs/2509.25738","url_pdf":"https://arxiv.org/pdf/2509.25738v1","authors":"[\"Tingmin Li\",\"Yixuan Li\",\"Yang Yang\"]","published":"2025-09-30T03:50:56Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}