EAGLE：面向以对象为中心的无监督语义分割的特征聚合学习
EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation
AI总结：
EAGLE提出EiCue谱线索与以对象为中心的对比损失，学习对象级表示以解决无监督语义分割中块级特征缺乏对象语义编码的问题，并在多个数据集上取得最优结果。
摘要：
语义分割长期以来依赖大量像素级标注数据，这促使了无监督方法的出现。其中，利用自监督视觉Transformer进行无监督语义分割（USS）凭借富有表现力的深度特征取得了稳步进展。然而，对于具有复杂对象的图像进行语义分割，一个主要挑战依然存在：块级特征中缺乏显式的对象级语义编码。这一技术局限往往导致对结构多样的复杂对象分割不充分。为弥补这一不足，我们提出了一种新方法EAGLE，该方法强调面向无监督语义分割的以对象为中心的表示学习。具体而言，我们引入了EiCue，这是一种谱技术，通过从深度图像特征的语义相似性矩阵和图像颜色亲和力中导出的特征基来提供语义和结构线索。此外，通过将我们的以对象为中心的对比损失与EiCue相结合，我们引导模型学习具有图像内和图像间对象特征一致性的对象级表示，从而提高语义准确性。在COCO-Stuff、Cityscapes和Potsdam-3数据集上的大量实验表明，EAGLE在复杂场景中实现了准确且一致的语义分割，取得了最先进的无监督语义分割结果。
英文摘要：
Semantic segmentation has innately relied on extensive pixel-level annotated data, leading to the emergence of unsupervised methodologies. Among them, leveraging self-supervised Vision Transformers for unsupervised semantic segmentation (USS) has been making steady progress with expressive deep features. Yet, for semantically segmenting images with complex objects, a predominant challenge remains: the lack of explicit object-level semantic encoding in patch-level features. This technical limitation often leads to inadequate segmentation of complex objects with diverse structures. To address this gap, we present a novel approach, EAGLE, which emphasizes object-centric representation learning for unsupervised semantic segmentation. Specifically, we introduce EiCue, a spectral technique providing semantic and structural cues through an eigenbasis derived from the semantic similarity matrix of deep image features and color affinity from an image. Further, by incorporating our object-centric contrastive loss with EiCue, we guide our model to learn object-level representations with intra- and inter-image object-feature consistency, thereby enhancing semantic accuracy. Extensive experiments on COCO-Stuff, Cityscapes, and Potsdam-3 datasets demonstrate the state-of-the-art USS results of EAGLE with accurate and consistent semantic segmentation across complex scenes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
