发表机构
Delft University of Technology; Southern Medical University; Tianjin University; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（代尔夫特理工大学; 南方医科大学; 天津大学; 中国科学院深圳先进技术研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对图像分割中细节丢失和边界模糊问题，提出融合先验知识与双域求解的FSANet网络，并引入含10种非理想场景的SceneX数据集，实验验证其高效有效。
AI 中文摘要
由于遮挡、光照不足和不规则结构，图像分割仍然具有挑战性。尽管基于Transformer的方法取得了较高的准确率，但它们严重依赖长距离空间特征，导致计算成本高，且忽略了先验知识或噪声模式，从而造成细节丢失和边界不清晰。为了解决这些问题，我们提出了频率空间感知网络（FSANet），它将先验知识与双域求解器相结合，以顺序适应不同的分割任务。具体来说，我们设计了三个关键模块：（1）结构先验模块，用于恢复被忽略的细节；（2）双域感知模块，用于在解耦噪声的同时捕获显著特征；（3）边缘估计模块，用于增强边缘感知以实现更精确的分割。此外，涵盖各种真实世界场景的综合分割数据集的有限可用性阻碍了现有方法的性能。为了解决这个问题，我们引入了SceneX，这是一个新颖的开源数据集，包含10个具有挑战性的非理想场景，为评估和提高分割模型的鲁棒性和实际适用性建立了新的基准。大量实验证明了FSANet的效率和有效性。
英文摘要
Image segmentation remains challenging due to occlusions, poor lighting, and irregular structures. Although transformer-based methods achieve high accuracy, they rely heavily on long-range spatial features, leading to high computational costs and neglecting prior knowledge or noise patterns, resulting in missing details and unclear boundaries. To address these issues, we propose Frequency Spatial Aware Network (FSANet), which integrates prior knowledge with a dual-domain solver to sequentially adapt to diverse segmentation tasks. Specifically, we design three key modules: (1) Structure Prior Module, which recovers overlooked details; (2) Dual-Domain Awareness Module, which captures salient features while disentangling noise; and (3) Edge Estimation Module, which enhances edge awareness for more precise segmentation. In addition, the limited availability of comprehensive segmentation datasets covering various real-world scenarios hinders the performance of existing methods. To address this, we introduce SceneX, a novel open-source dataset featuring 10 challenging non-ideal scenarios, establishing a new benchmark for evaluating and improving the robustness and real-world applicability of the segmentation models. Extensive experiments demonstrate the efficiency and effectiveness of FSANet.
Comments13 pages
