An Effective Solution for the CVPR 2026 8th UG2+ Challenge Track 3: Dynamic Object Segmentation in Turbulence

2026-05-30

AI Summary

This work addresses the challenge of dynamic object segmentation under severe geometric distortions and noise induced by atmospheric turbulence. Building upon the Segment Any Motion framework, the authors propose a domain adaptation strategy that leverages simulated turbulence perturbations during training, coupled with a spatiotemporal post-processing module designed to preserve small targets while enforcing label consistency across frames. The proposed approach effectively suppresses boundary artifacts and transient noise, achieving second place in Track 3 of the CVPR 2026 UG2+ Challenge. This demonstrates a significant improvement in both accuracy and robustness for segmenting moving objects in turbulent imaging conditions.

Abstract

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.

Problem

• Dynamic Object Segmentation
• Turbulence
• Atmospheric Distortion
• Motion Segmentation
• Video Segmentation

Innovation

• domain adaptation
• atmospheric turbulence
• spatio-temporal post-processing
• dynamic object segmentation
• data-centric augmentation