Abstract
Unsupervised segmentation approaches have increasingly leveraged foundation models (FM) to improve salient object discovery. However, these methods often falter in scenes with complex, multi-component morphologies, where fine-grained structural detail is indispensable. Many state-of-the-art unsupervised segmentation pipelines rely on mask discovery approaches that utilize coarse, patch-level representations. These coarse representations inherently suppress the fine-grained detail required to resolve such complex morphologies. To overcome this limitation, we propose Excite, Attend and Segment (EASe), an unsupervised domain-agnostic semantic segmentation framework for easy fine-grained mask discovery across challenging real-world scenes. EASe utilizes novel Semantic-Aware Upsampling with Channel Excitation (SAUCE) to excite low-resolution FM feature channels for selective calibration and attends across spatially-encoded image and FM features to recover full-resolution semantic representations. Finally, EASe segments the aggregated features into multi-granularity masks using a novel training-free Cue-Attentive Feature Aggregator (CAFE) which leverages SAUCE attention scores as a semantic grouping signal. EASe, together with SAUCE and CAFE, operate directly at pixel-level feature representations to enable accurate fine-grained dense semantic mask discovery. Our evaluation demonstrates superior performance of EASe over previous state-of-the-arts (SOTAs) across major standard benchmarks and diverse datasets with complex morphologies. Code is available at this https URL
Abstract (translated)
无监督分割方法日益借助基础模型（FM）来提升显著目标发现能力。然而，这些方法在结构复杂、多组分的形态场景中往往难以应对，而此类场景恰恰需要精细的结构细节。许多先进的无监督分割流程依赖基于粗略图块级表征的掩码发现方法，这类粗粒度表征本质上抑制了解决复杂形态所需的细粒度细节。为克服此限制，我们提出了Excite, Attend and Segment（EASe）——一个无监督、领域无关的语义分割框架，旨在实现挑战性真实场景中的便捷细粒度掩码发现。EASe采用新颖的语义感知通道激励上采样（SAUCE）技术，激发低分辨率FM特征通道以实现选择性校准，并跨空间编码的图像与FM特征进行注意力聚合，从而恢复全分辨率语义表征。最终，EASe通过新型免训练的线索感知特征聚合器（CAFE）将聚合特征分割为多粒度掩码，该模块利用SAUCE的注意力分数作为语义分组信号。EASe与SAUCE、CAFE共同直接在像素级特征表征上运作，实现精确的细粒度密集语义掩码发现。我们的评估表明，在主要标准基准及多样化的复杂形态数据集上，EASe性能优于此前先进方法（SOTAs）。代码已公开于此网址。
URL
https://arxiv.org/abs/2604.00276
