Query-guided Prototype Evolution Network for Few-Shot Segmentation
The author presents the Query-guided Prototype Evolution Network (QPENet) as a novel method to enhance Few-Shot Segmentation by integrating query features into prototype generation, resulting in customized solutions tailored to specific queries.
Query-guided Prototype Evolution Network for Few-Shot Segmentation: A Novel Approach Integrating Query Features for Customized Prototypes
Query-guided Prototype Evolution Network (QPENet) integrates query features to create customized prototypes for improved few-shot segmentation.
Query-guided Prototype Evolution Network for Few-Shot Segmentation
Query-guided Prototype Evolution Network (QPENet) integrates support and query features to create customized prototypes for precise segmentation.
Query-guided Prototype Evolution Network for Few-Shot Segmentation: A Novel Approach Integrating Query Features
Introducing the Query-guided Prototype Evolution Network (QPENet) to enhance Few-Shot Segmentation by integrating query features into prototype generation.
Prompt-and-Transfer: A Dynamic Class-aware Approach for Efficient Few-shot Segmentation
A novel prompt-driven scheme called "Prompt and Transfer" (PAT) is proposed to dynamically tune the encoder for focusing on class-specific objects in different Few-shot Segmentation tasks.
동적 클래스 인식 향상을 위한 프롬프트 및 전이 기법
본 논문은 프롬프트 기반 동적 클래스 인식 기법을 제안하여 적은 수의 지원 이미지로도 효과적으로 미지의 클래스를 세분화할 수 있다.
효율적인 지원 정보 활용을 위한 하이브리드 마바 네트워크
지원 정보의 효과적인 활용을 위해 지원 정보 재활용 마바와 쿼리 정보 차단 마바를 결합한 하이브리드 마바 네트워크를 제안한다.
Improving 3D Medical Image Segmentation with Limited Data: A Novel Inference-Time Pseudo-Labeling Approach
This paper introduces a novel inference-time pseudo-labeling technique to improve the performance of few-shot 3D medical image segmentation models, particularly in addressing the challenge of limited annotated data.
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