Few-Shot Semantic Segmentation Techniques
Summary
Few-shot semantic segmentation addresses the challenge of training models to delineate object classes in images when only a handful of labelled examples are available. It extends conventional semantic segmentation by enabling rapid adaptation to novel categories with minimal annotation effort. Core approaches include prototype-based methods that derive representative feature embeddings, meta-learning schemes that optimise model initialisations for fast fine-tuning, and mask- or attention-driven architectures that enhance the interaction between support and query images. Recent advances leverage self-distillation, multi-scale context fusion and edge-aware modules to improve accuracy and robustness across diverse domains, from medical imaging and remote sensing to autonomous driving and agricultural monitoring. By reducing annotation costs and boosting generalisation to unseen classes, few-shot methods promise transformative impact on real-world applications where data scarcity remains a barrier.
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Few-Shot Semantic Segmentation Techniques publication trend
The graph below shows the total number of articles in few-shot semantic segmentation techniques across all publications each year (not limited to Nature Index journals).
Technical terms
Few-shot semantic segmentation: Task of segmenting image regions into classes using only a few labelled examples per novel category.
Prototype: Representative feature vector summarising foreground or background characteristics for a given class.
Support set / Query set: Support set comprises images with annotated masks used for model conditioning; the query set contains unlabelled images to be segmented.
Meta-learning: Learning paradigm that optimises model initialisations or update rules to enable rapid adaptation with limited data.
Mask aggregation: Approach that generates multiple mask proposals with associated probabilities and combines them to form the final segmentation.
References
- PCNet: Leveraging Prototype Complementarity to Improve Prototype Affinity for Few-Shot Segmentation. Electronics (2023).
- Few-Shot Semantic Segmentation via Mask Aggregation. Neural Processing Letters (2024).
- MCEENet: Multi-Scale Context Enhancement and Edge-Assisted Network for Few-Shot Semantic Segmentation. Sensors (2023).
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