Xin-Yi Zhang , Xian-Kai Lu , Yi-Long Yin , Han-Jia Ye , De-Chuan Zhan
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引用次数: 0
Abstract
Few-shot segmentation (FSS) is a longstanding challenge in computer vision. Previous methods adopting Gaussian Processes (GPs) aggregate detailed information and manage complex distributions from small support sets, thereby modeling uncertainty of features and handling wide variations in context. However, the exact GP-based FSS methods struggle with computational burden and information redundancy. To tackle the issues, we propose ESGP, an Efficient Sampling-based Gaussian Process framework for few-shot segmentation. The model decouples the GP into a two-step process: weight space approximation for the prior and function space update for the posterior. Additionally, we adopt Deep Kernel Learning to enhance ESGP’s performance. This combination results in a faster, more accurate FSS model that effectively concentrates support sample information. Moreover, GP’s inherent ability to model uncertainty provides robust predictions and valuable insights into segmentation reliability. Experimental results demonstrate that ESGP outperforms previous GP-based methods and achieves competitive performance with state-of-the-art techniques.
期刊介绍:
The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.