CSS-Segment: 2nd Place Report of LSVOS Challenge VOS Track
원문 초록
Video object segmentation is a challenging task that serves as the cornerstone of numerous downstream applications, including video editing and autonomous driving. In this technical report, we briefly introduce the solution of our team "yuanjie" for video object segmentation in the 6-th LSVOS Challenge VOS Track at ECCV 2024. We believe that our proposed CSS-Segment will perform better in videos of complex object motion and long-term presentation. In this report, we successfully validated the effectiveness of the CSS-Segment in video object segmentation. Finally, our method achieved a J\&F score of 80.84 in and test phases, and ultimately ranked 2nd in the 6-th LSVOS Challenge VOS Track at ECCV 2024.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
NOCTIS: Novel Object Cyclic Threshold based Instance Segmentation
SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction
MUVOD: A Novel Multi-view Video Object Segmentation Dataset and A Benchmark for 3D Segmentation
NRSeg: Noise-Resilient Learning for BEV Semantic Segmentation via Driving World Models
FactSeg: Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery
A Convolutional Neural Network for Point Cloud Instance Segmentation in Cluttered Scene Trained by Synthetic Data Without Color
토론
이 논문과 연결된 포럼 글이 아직 없습니다.