EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation
Core Concepts
EAGLE introduces a novel approach, emphasizing object-centric representation learning for unsupervised semantic segmentation, addressing the challenge of inadequate segmentation of complex objects with diverse structures.
Abstract
EAGLE presents a unique method, EiCue, providing semantic and structural cues through an eigenbasis derived from deep image features. By incorporating object-centric contrastive loss with EiCue, the model learns object-level representations enhancing semantic accuracy. Extensive experiments demonstrate state-of-the-art results on various datasets. The method outperforms existing approaches by accurately segmenting objects and preserving details.
EAGLE
Stats
EAGLE showcases substantial improvements over existing methods in unsupervised accuracy.
The linear accuracy and mIoU of EAGLE bring notable improvements over existing methods.
EAGLE significantly improves both unsupervised Acc. and mIoU on the Cityscapes dataset.
Quotes
"EAGLE emphasizes object-centric representation learning for unsupervised semantic segmentation."
"EiCue provides semantic and structural cues through an eigenbasis derived from deep image features."