针对湍流干扰下的动态目标分割，提出数据增强与时空后处理方案。
A Trajectory-Driven Spatio-Temporal Refinement Solution for CVPR 2026 8th UG2+ Challenge Track 3: DOST
- 用DAVIS数据+模拟湍流增强训练数据，提升模型鲁棒性
- 引入时空后处理模块，消除误分割与碎片噪声
- 有效保留小目标和帧间标签一致性，适合复杂场景应用
本文针对CVPR 2026 UG2+挑战赛第3赛道——湍流环境下的动态目标分割（DOST），提出解决方案。基于强大的基础框架SegAnyMo，我们提出两项关键改进：首先采用数据驱动的领域自适应策略，通过融合DAVIS数据集的部分序列与DOST数据集子集，并施加模拟大气扰动退化，显著扩充训练数据并增强模型对复杂几何畸变的鲁棒性；其次引入时空后处理模块，有效剔除持续存在的边界连接伪前景和短时碎片噪声，同时严格保留真实小目标并维持各帧间个体标签的一致性。结合上述策略，本方法在挑战赛中取得第二名的成绩。
原文摘要 · Abstract (English)
In this work, we present our solution for the 8th UG2+ Challenge (CVPR 2026) Track 3: Dynamic Object Segmentation in Turbulence (DOST). Our method is built upon the strong baseline framework Segment Any Motion (SegAnyMo), which provides powerful mask generation and motion tracking capabilities. To further boost the segmentation performance under severe atmospheric distortions, we propose two key improvements. First, we employ a data-centric domain adaptation strategy. We significantly expand our training data by incorporating selected sequences from the DAVIS dataset alongside a subset of the DOST dataset, and apply simulated atmospheric fluctuation degradations to enhance the model's robustness against complex geometric distortions. Second, we introduce a spatio-temporal post-processing module. This refinement step effectively removes persistent boundary-connected false foregrounds and short-lived fragmented noise, while strictly preserving genuine small targets and maintaining original individual labels across frames. With these combined strategies, our proposed method ranks the 2st place in the challenge.
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