FCC: Fully Connected Correlation for One-Shot Segmentation
Moon, Seonghyeon Kong, Haein Khan, Muhammad Haris Kapadia, Mubbasir Lin, Yuewei
Moon, Seonghyeon
Kong, Haein
Khan, Muhammad Haris
Kapadia, Mubbasir
Lin, Yuewei
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Department
Computer Vision
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Workshop
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English
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Abstract
One-shot segmentation (OSS) aims to segment the target object in a query image using only one set of support image and mask. Therefore, having strong prior information for the target object using the support set is essential to guide the initial training of OSS, which leads to the success of one-shot segmentation in challenging cases, such as when the target object shows considerable variation in appearance, texture, or scale across the support and query images. To enrich this prior knowledge, we introduce FCC (Fully Connected Correlation) which integrates pixel-level correlations between support and query features, capturing associations that reveal target-specific patterns and correspondences in both same-layers and cross-layers. FCC captures previously inaccessible target information, effectively addressing the limitations of support mask. Our approach consistently demonstrates state-of-the-art performance in the PASCAL, COCO, and domain shift tests, while also notably accelerating model convergence. We conducted an ablation study and cross-layer correlation analysis to validate FCC’s core methodology. These findings reveal the effectiveness of FCC in enhancing prior information and overall model performance for OSS1.
Citation
S. Moon, H. Kong, M.H. Khan, M. Kapadia, Y. Lin, "FCC: Fully Connected Correlation for One-Shot Segmentation," 2026, pp. 4827-4837.
Source
IEEE Workshop on Applications of Computer Vision (WACV)
Conference
2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Keywords
46 Information and Computing Sciences, 4605 Data Management and Data Science
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Source
2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Publisher
IEEE