SkySense-VITA is introduced, a unified in-context segmentation model, which synergistically processes both optical and Synthetic Aperture Radar (SAR) imagery using VI sual, T extu A l, or fused prompts, and is proposed to de-couple multi-modal prompt fusion and prediction process.
Fusing optical imagery with complementary modalities (X-modality), such as light detection and ranging (LiDAR) and synthetic aperture radar (SAR), is essential for robust semantic segmentation in complex environments. Although recent modality-agnostic models improve generalizability beyond fixed-pair methods, they stil...
Xu-Ming Zhang, N. Yokoya, Xing-Fa Gu et al.· IEEE Transactions on Geoscie...· 0 citations
A progressive layer-wise contrastive learning framework (MTC-Net) that couples the pseudo-label with the network’s representational hierarchy, forming a curriculum from local texture robustness to global semantic invariance.
Xiao Xiao, Han Zhang, Kenan Cheng et al.· Remote Sensing· 0 citations
A three-stage framework that integrates complementary priors from Contrastive Language–Image Pre-training, Self-Distillation with No Labels version 2 (DINOv2), and the Segment Anything Model (SAM) is proposed, demonstrating the effectiveness and generalizability of the proposed framework across diverse remote sensing s...
A unified collaborative dual-task learning framework that jointly optimizes S2O image translation and semantic segmentation through a shared hierarchical Vision Transformer is proposed, which achieves competitive S2O translation quality and semantic segmentation performance.
Si-Yuan Liu, Xu-Ze Zhang, Yong-Shun Wang et al.· 0 citations
DLPANet is proposed, a novel dual-level prototype alignment network centered on Prototype-Guided Spatial Attention, enabling simultaneous modeling of scene context and fine-grained details and demonstrates that the decoupled dual cross-attention mechanism provides superior prototype-query alignment compared to prior gl...
Mustafa Alawadi, M. Fateh· Jordanian Journal of Compute...· 0 citations
This paper establishes the first cross-domain RRSIS benchmark, designated as the Vaihingen-Potsdam Referring (VPRef) dataset, and develops a tailored parameter-efficient domain adaptation baseline anchored on the Segment Anything Model (SAM3) via Low-Rank Adaptation (LoRA).
Quan-Wei Liu, Tao Huang, Jia-Qi Yang et al.· 0 citations
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