Unsupervised Domain Adaptation for Adverse Weather Semantic Segmentation (UDA-ASS) aims to transfer semantic knowledge from labeled normal-weather images to unlabeled adverse environments. Existing approaches implicitly assume that restoration and segmentation provide mutually beneficial guidance. However, under severe degradation and without target-domain supervision, the validity of cross-task optimization directions becomes fundamentally unidentifiable, leading to hallucination-driven error propagation. In this work, we propose a novel Unsupervised Restoration-Segmentation Collaborative Learning Framework (Ultra), which reframes cross-task interaction as direction selection under uncertainty and causal effect estimation, enabling reliable collaboration through candidate direction generation and intervention-based filtering. In detail, we propose CTDN and CMIL. The former exploits complementary visual structures and semantic information to generate candidate optimization directions and performs cooperative direction selection between restoration and segmentation. The latter reformulates cross-task information transfer from correlation-based propagation into causal effect assessment, suppressing hallucination propagation. Extensive experiments on three widely used UDA-ASS benchmarks demonstrate state-of-the-art segmentation performance. Beyond segmentation, our framework achieves better unsupervised restoration results than existing UDA-ASS restoration methods and generalizes to unsupervised restoration and object detection collaboration tasks. Code and models will be available at https://github.com/Wang-Shiqin/Ultra.
Shiqin Wang, Zhiqi Li, Haoyuan Du et al.· 0 citations
Vision–language pretrained models, particularly CLIP, have demonstrated remarkable zero-shot transfer capabilities across various image-level tasks, catalyzing the advancement of open-vocabulary semantic segmentation (OVSS) in remote sensing (RS). However, the direct deployment of CLIP to the RS domain is inherently constrained by the profound domain shift between terrestrial and overhead perspectives, as well as the intricate geometric heterogeneities regarding scale and orientation. To circumvent these limitations, we propose CDSeg, a robust framework tailored for RSOVSS. Central to this architecture is the dual-domain feature compensation module (DDFCM), which integrates DINOv3 weights, pretrained on large-scale RS benchmarks, to augment CLIP with domain-specific semantic priors, effectively bridging the natural-to-satellite knowledge gap. Furthermore, we introduce a MambaVision-driven cross-feature fine-grained interaction module (CFFIM) to facilitate a unified refinement of spatial and category attributes, leveraging long-range dependency modeling to enhance the model’s discriminative power in unseen environments. To robustly manage the complexities of diverse orientations and scales, CDSeg incorporates a direction-aware rotation strategy and a wavelet-cross-attention-enhanced module (WCAEM) for high-fidelity multiscale feature decoding. Empirical evaluations on four public benchmarks demonstrate that CDSeg achieves state-of-the-art (SOTA) performance, while extensive ablation studies substantiate the synergistic contribution and indispensability of each component.
Jiayuan Li, Zhen Wang, Xiao Sun et al.· IEEE Transactions on Geoscie...· 0 citations
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