mlsanityData to 14 Oct 2026

Query-guided Prototype Evolution Network for Few-Shot Segmentation

arXiv 2403.06488 ↗First posted 11 Mar 2024cs.CVcited by 12

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Abstract (arXiv)

Previous Few-Shot Segmentation (FSS) approaches exclusively utilize support features for prototype generation, neglecting the specific requirements of the query. To address this, we present the Query-guided Prototype Evolution Network (QPENet), a new method that integrates query features into the generation process of foreground and background prototypes, thereby yielding customized prototypes attuned to specific queries. The evolution of the foreground prototype is accomplished through a textitsupport-query-support iterative process involving two new modules: Pseudo-prototype Generation (PPG) and Dual Prototype Evolution (DPE). The PPG module employs support features to create an initial prototype for the preliminary segmentation of the query image, resulting in a pseudo-prototype reflecting the unique needs of the current query. Subsequently, the DPE module performs reverse segmentation on support images using this pseudo-prototype, leading to the generation of evolved prototypes, which can be considered as custom solutions. As for the background prototype, the evolution begins with a global background prototype that represents the generalized features of all training images. We also design a Global Background Cleansing (GBC) module to eliminate potential adverse components mirroring the characteristics of the current foreground class. Experimental results on the PASCAL-5i and COCO-20i datasets attest to the substantial enhancements achieved by QPENet over prevailing state-of-the-art techniques, underscoring the validity of our ideas.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 44 of its 71 references that are in the map. First posted on arXiv 11 Mar 2024.

Builds on

44 of 71 references.

References · 44

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32 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 4; 2026: 5.

12 papers.

  1. 2024Strike a Balance in Continual Panoptic Segmentationcited by 10
  2. 2024MSDNet: Multi-Scale Decoder for Few-Shot Semantic Segmentation via Transformer-Guided Prototypingcited by 5
  3. 2025From Sight to Insight: Unleashing Eye-Tracking in Weakly Supervised Video Salient Object Detectioncited by 3
  4. 2025Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentationcited by 1
  5. 2026RDNet: Region Proportion-Aware Dynamic Adaptive Salient Object Detection Network in Optical Remote Sensing Imagescited by 1
  6. 2024Symmetrical Joint Learning Support-query Prototypes for Few-shot Segmentationcited by 0
  7. 2025Open-world Point Cloud Semantic Segmentation: A Human-in-the-loop Frameworkcited by 0
  8. 2025Multi-Text Guided Few-Shot Semantic Segmentationcited by 0
  9. 2026Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learningcited by 0
  10. 2026G2HFNet: GeoGran-Aware Hierarchical Feature Fusion Network for Salient Object Detection in Optical Remote Sensing Imagescited by 0
  11. 2026RSONet: Region-guided Selective Optimization Network for RGB-T Salient Object Detectioncited by 0
  12. 2026P3-SAM: SAM with Perceptual Parallel Prompt for Few-Shot Strip Steel Surface Defect Segmentationcited by 0