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One-Shot Learning for Semantic Segmentation

https://doi.org/10.5244/c.31.167

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Learning to Learn: Model Regression Networks for Easy Small Sample Learning

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Cross-Generalization: Learning Novel Classes from a Single Example by Feature Replacement

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Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs

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ImageNet: A large-scale hierarchical image database

inferredcites →https://doi.org/10.1109/cvpr.2014.261openalex-snapshot

Enriching Visual Knowledge Bases via Object Discovery and Segmentation

inferredcites →https://doi.org/10.1109/cvpr.2014.81openalex-snapshot

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

inferredcites →https://doi.org/10.1109/cvpr.2015.7298642openalex-snapshot

Hypercolumns for object segmentation and fine-grained localization

inferredcites →https://doi.org/10.1109/cvpr.2015.7298959openalex-snapshot

Feedforward semantic segmentation with zoom-out features

inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot

Fully convolutional networks for semantic segmentation

inferredcites →https://doi.org/10.1109/cvpr.2016.11openalex-snapshot

Image Question Answering Using Convolutional Neural Network with Dynamic Parameter Prediction

inferredcites →https://doi.org/10.1109/cvpr.2016.349openalex-snapshot

Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network

inferredcites →https://doi.org/10.1109/cvpr.2016.492openalex-snapshot

Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform

inferredcites →https://doi.org/10.1109/iccv.2013.164openalex-snapshot

Co-segmentation by Composition

inferredcites →https://doi.org/10.1109/iccv.2015.178openalex-snapshot

Learning Deconvolution Network for Semantic Segmentation

inferredcites →https://doi.org/10.1109/iccv.2015.191openalex-snapshot

BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation

inferredcites →https://doi.org/10.1109/iccv.2015.209openalex-snapshot

Constrained Convolutional Neural Networks for Weakly Supervised Segmentation

inferredcites →https://doi.org/10.1109/tpami.2006.79openalex-snapshot

One-shot learning of object categories

inferredcites →https://doi.org/10.1109/tpami.2017.2699184openalex-snapshot

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

inferredcites →https://doi.org/10.1126/science.aab3050openalex-snapshot

Human-level concept learning through probabilistic program induction

inferredcites →https://doi.org/10.48550/arxiv.1407.1808openalex-snapshot

Simultaneous Detection and Segmentation

inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot

Very Deep Convolutional Networks for Large-Scale Image Recognition

inferredcites →https://doi.org/10.48550/arxiv.1412.7144openalex-snapshot

Fully Convolutional Multi-Class Multiple Instance Learning

inferredcites →https://doi.org/10.48550/arxiv.1502.02734openalex-snapshot

Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation

inferredcites →https://doi.org/10.48550/arxiv.1504.04788openalex-snapshot

Compressing Neural Networks with the Hashing Trick

inferredcites →https://doi.org/10.48550/arxiv.1606.04080openalex-snapshot

Matching Networks for One Shot Learning

inferredcites →https://doi.org/10.48550/arxiv.1606.05233openalex-snapshot

Learning feed-forward one-shot learners

inferredcites →https://doi.org/10.48550/arxiv.1608.08614openalex-snapshot

What makes ImageNet good for transfer learning?

inferredcites →https://doi.org/10.48550/arxiv.1703.03129openalex-snapshot

Learning to Remember Rare Events

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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An Enhanced Approach for Few-Shot Segmentation Via Smooth Downsampling Mask and Label Smoothing Loss

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Mask-Free Iterative Refinement Network for Weakly-Supervised Few-Shot Semantic Segmentation

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A Partial Context-Aware Structure for Few-Shot Semantic Segmentation

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Multimodal Hierarchical Attention Framework for Efficient Weakly Supervised Few‐Shot Segmentation Under SAGIN Environment

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A Convolutional Neural Network Method for Boundary Optimization Enables Few-Shot Learning for Biomedical Image Segmentation

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A New Local Transformation Module for Few-Shot Segmentation

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Deep Similarity Fusion Networks for One-Shot Semantic Segmentation

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Few Shot Learning for Medical Imaging

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Self-supervision with Superpixels: Training Few-Shot Medical Image Segmentation Without Annotation

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Part-Aware Prototype Network for Few-Shot Semantic Segmentation

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Prototype Mixture Models for Few-Shot Semantic Segmentation

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Few-Shot Semantic Segmentation with Democratic Attention Networks

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Learning to Segment Anatomical Structures Accurately from One Exemplar

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Learning to Adapt to Unseen Abnormal Activities Under Weak Supervision

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An Analysis of One-Shot Augmented Learning: A Face Recognition Case Study

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Few-Shot Segmentation via Complementary Prototype Learning and Cascaded Refinement

inferred← citeshttps://doi.org/10.1007/978-3-030-88210-5_15openalex-snapshot

One-Shot Learning for Landmarks Detection

inferred← citeshttps://doi.org/10.1007/978-3-031-15934-3_3openalex-snapshot

BPGG: Bidirectional Prototype Generation and Guidance Network for Few-Shot Anomaly Localization

inferred← citeshttps://doi.org/10.1007/978-3-031-16440-8_49openalex-snapshot

Few-shot Medical Image Segmentation Regularized with Self-reference and Contrastive Learning

inferred← citeshttps://doi.org/10.1007/978-3-031-16961-8_10openalex-snapshot

Few-Shot Segmentation of Microscopy Images Using Gaussian Process

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Few-Shot Segmentation via Rich Prototype Generation and Recurrent Prediction Enhancement

inferred← citeshttps://doi.org/10.1007/978-3-031-19800-7_41openalex-snapshot

Self-support Few-Shot Semantic Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-19818-2_13openalex-snapshot

Dense Gaussian Processes for Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-19818-2_21openalex-snapshot

Interclass Prototype Relation for Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-19818-2_3openalex-snapshot

Adaptive Agent Transformer for Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-19818-2_7openalex-snapshot

Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-20044-1_1openalex-snapshot

tSF: Transformer-Based Semantic Filter for Few-Shot Learning

inferred← citeshttps://doi.org/10.1007/978-3-031-20044-1_24openalex-snapshot

Dual Contrastive Learning with Anatomical Auxiliary Supervision for Few-Shot Medical Image Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-20044-1_29openalex-snapshot

HM: Hybrid Masking for Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-20044-1_8openalex-snapshot

Doubly Deformable Aggregation of Covariance Matrices for Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-20044-1_9openalex-snapshot

Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-20056-4_5openalex-snapshot

Cross-Domain Few-Shot Semantic Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-20074-8_33openalex-snapshot

VizWiz-FewShot: Locating Objects in Images Taken by People with Visual Impairments

inferred← citeshttps://doi.org/10.1007/978-3-031-20497-5_4openalex-snapshot

Exploring Hierarchical Prototypes for Few-Shot Segmentation

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A Joint Framework Towards Class-aware and Class-agnostic Alignment for Few-shot Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-46005-0_14openalex-snapshot

Self-supervised Few-Shot Learning for Semantic Segmentation: An Annotation-Free Approach

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The Cadmium in Soil and Plants

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Q-Net: Query-Informed Few-Shot Medical Image Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-50075-6_6openalex-snapshot

4RATFNet: Four-Dimensional Residual-Attention Improved-Transfer Few-Shot Semantic Segmentation Network for Landslide Detection

inferred← citeshttps://doi.org/10.1007/978-3-031-50269-9_20openalex-snapshot

Deep Network-Based Computational Transfer of Artistic Style in Art Analysis

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“Car or Bus?" CLearSeg: CLIP-Enhanced Discrimination Among Resembling Classes for Few-Shot Semantic Segmentation

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Contextual Augmentation with Bias Adaptive for Few-Shot Video Object Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-67751-9_9openalex-snapshot

SnapSeg: Training-Free Few-Shot Medical Image Segmentation with Segment Anything Model

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Few-Shot 3D Volumetric Segmentation with Multi-surrogate Fusion

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Measuring Affinity: Similarity-Based Auxiliary Unlabeled Guidance for Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-72390-2_26openalex-snapshot

Reducing Annotation Burden: Exploiting Image Knowledge for Few-Shot Medical Video Object Segmentation via Spatiotemporal Consistency Relearning

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Eliminating Feature Ambiguity for Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-72764-1_22openalex-snapshot

Pseudo-embedding for Generalized Few-Shot 3D Segmentation

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Finding Visual Task Vectors

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Rethinking and Improving Visual Prompt Selection for In-Context Learning Segmentation

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Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the Wild

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Collaborative Vision-Text Representation Optimizing for Open-Vocabulary Segmentation

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MetaWeather: Few-Shot Weather-Degraded Image Restoration

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Self-Supervised Contrastive Learning for Consistent Few-Shot Image Representations

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Correlation Weighted Prototype-Based Self-supervised One-Shot Segmentation of Medical Images

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Sketch2Seg: Sketch-Based Image Segmentation with Pre-trained Diffusion Model

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Success Stories of QSAR Methods in Drug Design

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Machine Learning for Active Assisted Living: Core Concepts, Challenges, and Solutions

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Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRI

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Harnessing Few-Shot Learning Segmentation for Histopathology: A Comprehensive Practical Study

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One Shot, Few Perspectives: Ensemble Learning for Image Segmentation

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FS-SAM2: Adapting Segment Anything Model 2 for Few-Shot Semantic Segmentation via Low-Rank Adaptation

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Efficient Masked Attention Transformer for Few-Shot Classification and Segmentation

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A Divide-and-Conquer Network for Generalized Few-Shot Segmentation

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Reverse-Calibrated Prototype-Guided Few-Shot Semantic Segmentation Network

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Mitigating Pose-Scale Discrepancy Bias and Reforming Multi-support Reasoning for Few-Shot Semantic Segmentation

inferred← citeshttps://doi.org/10.1007/978-981-15-8155-7_166openalex-snapshot

Few-Shot Semantic Segmentation for Building Detection and Road Extraction Based on Remote Sensing Imagery Using Model-Agnostic Meta-Learning

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The Ikshana Hypothesis of Human Scene Understanding

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CAMO-InstSynth: Few-Shot Camouflage Instance Segmentation with Multi-conditional Background Synthesis and Generative Augmentation

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GLDNet: Global-Local Prototype Enhancement and Dense Feature Aggregation for Few-Shot Aerial Image Segmentation

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Global-Aware Multi-scale Hybrid Mamba Network for Few-Shot Semantic Segmentation

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Few-shot Semantic Segmentation with Multi-scale Foreground Guidance and Dynamic Prototype Alignment

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SDG: Semantic Saliency-Guided Prototypical Graph Network for Few-Shot Segmentation

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Context Remote Sensing Segment Anything Model

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Generalized Prototype-Based Few-Shot Semantic Segmentation Network

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Unlocking Multimodal Potential for Few-Shot Semantic Segmentation with Vision-Enriched Text

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Generalized Few-Shot Semantic Segmentation Based on Relevant Intrinsic Feature Enhancement

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Co-support Few-Shot Segmentation via Internal Complement and Intra Balance

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Prototype Enhancement for Few-Shot Point Cloud Semantic Segmentation

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Feature-Guided Prototype-Enhanced Few-Shot Semantic Segmentation Model

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A Classifier-Based Two-Stage Training Model for Few-Shot Segmentation

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CSAANet: An Attention-Based Mechanism for Aligned Few-Shot Semantic Segmentation Network

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TASFormer: Task-Aware Image Segmentation Transformer

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Global-guided cross-reference network for co-salient object detection

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Query semantic reconstruction for background in few-shot segmentation

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Multi-prototype collaborative perception enhancement network for few-shot semantic segmentation

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Learning few-shot semantic segmentation with error-filtered segment anything model

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Few-shot medical image segmentation via query transformation learning

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Hybrid token learning with bidirectional attention for few-shot semantic segmentation

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Multi-scale attentional similarity guidance network for few-shot semantic segmentation

inferred← citeshttps://doi.org/10.1007/s00521-022-07654-yopenalex-snapshot

Kernel-based similarity sorting and allocation for few-shot semantic segmentation

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MMPL-Net: multi-modal prototype learning for one-shot RGB-D segmentation

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Few-shot semantic segmentation: a review on recent approaches

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Few-shot video object segmentation with prototype evolution

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A lightweight siamese transformer for few-shot semantic segmentation

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Self-supervised few-shot medical image segmentation with spatial transformations

inferred← citeshttps://doi.org/10.1007/s00530-025-01718-4openalex-snapshot

Dual-branch aggregation and edge refinement network for few shot semantic segmentation

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Generalized few-shot semantic segmentation via contrastive learning and orthogonal decoupling

inferred← citeshttps://doi.org/10.1007/s00530-026-02244-7openalex-snapshot

CSPGNet: few-shot segmentation in X-ray image based on cross-attention and self-adaptive prototype guidance

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ReLiFSS: reliability-aware few-shot medical image segmentation via query pseudo-prototype and reverse support segmentation

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A Prior-mask-guided Few-shot Learning for Skin Lesion Segmentation

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Few-Shot Quaternion-valued Correlation Squeeze Network for Document Image Layout Segmentation

inferred← citeshttps://doi.org/10.1007/s10044-025-01547-1openalex-snapshot

Few-shot segmentation network based on class-aware prototype fusion

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FS-DANet: Dual-Domain Signal Enhancement and Dynamic Spatial Calibration for Gastric Ultrasound Artifact Mitigation

inferred← citeshttps://doi.org/10.1007/s10489-021-02976-1openalex-snapshot

URO-GAN: An untrustworthy region optimization approach for adipose tissue segmentation based on adversarial learning

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Joint model- and immunohistochemistry-driven few-shot learning scheme for breast cancer segmentation on 4D DCE-MRI

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A novel inference paradigm based on multi-view prototypes for one-shot semantic segmentation

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Simple yet effective joint guidance learning for few-shot semantic segmentation

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EFTNet: an efficient fine-tuning method for few-shot segmentation

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SANet: similarity aggregation and semantic fusion for few-shot semantic segmentation

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Multi-modal background-aware for defect semantic segmentation with limited data

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Hierarchical adaptive correlation network for few-shot metal surface defect segmentation with cross-domain generalization

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Distance-guided prototype network for few-shot vision-based industrial defect detection

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Res2-UNeXt: a novel deep learning framework for few-shot cell image segmentation

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Mining semantic information from intra-image and cross-image for few-shot segmentation

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TPN: Triple parts network for few-shot instance segmentation

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Few-shot semantic segmentation via multi-level feature extraction and multi-prototype localization

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Learning discriminative foreground-and-background features for few-shot segmentation

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FSTL-SA: few-shot transfer learning for sentiment analysis from facial expressions

inferred← citeshttps://doi.org/10.1007/s11042-025-20673-wopenalex-snapshot

Dual-level correspondence network for few-shot semantic segmentation

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Few-Shot Semantic Segmentation via Mask Aggregation

inferred← citeshttps://doi.org/10.1007/s11263-022-01642-4openalex-snapshot

One-Shot Object Affordance Detection in the Wild

inferred← citeshttps://doi.org/10.1007/s11263-022-01677-7openalex-snapshot

CRCNet: Few-Shot Segmentation with Cross-Reference and Region–Global Conditional Networks

inferred← citeshttps://doi.org/10.1007/s11263-023-01886-8openalex-snapshot

Few-Shot Segmentation via Divide-and-Conquer Proxies

inferred← citeshttps://doi.org/10.1007/s11263-024-02145-0openalex-snapshot

Towards Task Sampler Learning for Meta-Learning

inferred← citeshttps://doi.org/10.1007/s11263-024-02165-wopenalex-snapshot

Multi-modal Prototypes for Open-World Semantic Segmentation

inferred← citeshttps://doi.org/10.1007/s11263-024-02252-yopenalex-snapshot

AgMTR: Agent Mining Transformer for Few-Shot Segmentation in Remote Sensing

inferred← citeshttps://doi.org/10.1007/s11263-025-02390-xopenalex-snapshot

Temporal Transductive Inference for Few-Shot Video Object Segmentation

inferred← citeshttps://doi.org/10.1007/s11263-025-02397-4openalex-snapshot

Investigating Self-Supervised Methods for Label-Efficient Learning

inferred← citeshttps://doi.org/10.1007/s11263-025-02444-0openalex-snapshot

Few-Shot Referring Video Single- and Multi-Object Segmentation Via Cross-Modal Affinity with Instance Sequence Matching

inferred← citeshttps://doi.org/10.1007/s11276-021-02713-zopenalex-snapshot

Boosting and rectifying few-shot learning prototype network for skin lesion classification based on the internet of medical things

inferred← citeshttps://doi.org/10.1007/s11465-025-0863-1openalex-snapshot

DEGRNet: An innovative dual-branch edge-based graph reasoning network for the few-shot segmentation of industrial surface defects

inferred← citeshttps://doi.org/10.1007/s11554-025-01751-6openalex-snapshot

GF-few: a real-time lightweight Gaussian feature enhanced few-shot network for industrial defect detection

inferred← citeshttps://doi.org/10.1007/s11760-024-03040-4openalex-snapshot

Few-shot segmentation based on high-resolution representation and Brownian distance covariance learning

inferred← citeshttps://doi.org/10.1007/s11760-024-03531-4openalex-snapshot

MIINet: a multi-branch information interaction network for few-shot segmentation

inferred← citeshttps://doi.org/10.1007/s11760-025-03878-2openalex-snapshot

Frequency separation-based few-shot segmentation

inferred← citeshttps://doi.org/10.1007/s11760-026-05256-yopenalex-snapshot

Dual-branch feature continual learning for few-shot semantic segmentation

inferred← citeshttps://doi.org/10.1007/s11760-026-05752-1openalex-snapshot

EIQNet: extracting key support information from query images: a novel idea for few-shot segmentation

inferred← citeshttps://doi.org/10.1007/s12559-021-09990-yopenalex-snapshot

FFNet: Feature Fusion Network for Few-shot Semantic Segmentation

inferred← citeshttps://doi.org/10.1007/s12559-024-10273-5openalex-snapshot

Category-Aware Siamese Learning Network for Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1007/s12652-022-04327-5openalex-snapshot

Multi-level adaptive few-shot learning network combined with vision transformer

inferred← citeshttps://doi.org/10.1007/s13735-023-00282-9openalex-snapshot

Hierarchical bidirectional aggregation with prior guided transformer for few-shot segmentation

inferred← citeshttps://doi.org/10.1007/s40747-024-01539-4openalex-snapshot

Prototype as query for few shot semantic segmentation

inferred← citeshttps://doi.org/10.1007/s42452-025-08188-3openalex-snapshot

The evolving landscape of few shot learning in medical image diagnosis a scoping review

inferred← citeshttps://doi.org/10.1007/s43684-023-00058-2openalex-snapshot

Distilling base-and-meta network with contrastive learning for few-shot semantic segmentation

inferred← citeshttps://doi.org/10.1007/s44267-025-00102-0openalex-snapshot

Visual-guided human-object interaction detection

inferred← citeshttps://doi.org/10.1016/b978-0-323-89931-4.00010-9openalex-snapshot

Meta-learning basics and background

inferred← citeshttps://doi.org/10.1016/j.adhoc.2019.102055openalex-snapshot

Few shot learning for multi-class classification based on nested ensemble DSVM

inferred← citeshttps://doi.org/10.1016/j.aei.2024.102613openalex-snapshot

Few-shot unseen defect segmentation for polycrystalline silicon panels with an interpretable dual subspace attention variational learning framework

inferred← citeshttps://doi.org/10.1016/j.asr.2024.08.069openalex-snapshot

Cross-domain few-shot semantic segmentation for the astronaut work environment

inferred← citeshttps://doi.org/10.1016/j.bspc.2024.107069openalex-snapshot

OSAM-Fundus: A training-free, one-shot segmentation framework for optic disc and cup in fundus images

inferred← citeshttps://doi.org/10.1016/j.bspc.2025.108500openalex-snapshot

Multi-level hierarchical prototype for few-shot medical image segmentation

inferred← citeshttps://doi.org/10.1016/j.bspc.2025.108918openalex-snapshot

Edge-aware multi-prototype learning for few-shot medical image segmentation

inferred← citeshttps://doi.org/10.1016/j.bspc.2025.108986openalex-snapshot

Robust Few-Shot segmentation for multi-modal medical images using Fourier-Guided style fusion and prototype alignment

inferred← citeshttps://doi.org/10.1016/j.bspc.2026.110562openalex-snapshot

Complexity-aware adaptive prototype network with multi-scale attention for self-supervised few-shot medical image segmentation

inferred← citeshttps://doi.org/10.1016/j.cag.2023.05.009openalex-snapshot

An overview on Meta-learning approaches for Few-shot Weakly-supervised Segmentation

inferred← citeshttps://doi.org/10.1016/j.compag.2021.106122openalex-snapshot

Small-sample learning with salient-region detection and center neighbor loss for insect recognition in real-world complex scenarios

inferred← citeshttps://doi.org/10.1016/j.compag.2026.111524openalex-snapshot

Balancing accuracy and cost in precision agriculture: a few-shot learning approach for efficient weed − crop segmentation

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2021.105067openalex-snapshot

Few-shot medical image segmentation using a global correlation network with discriminative embedding

inferred← citeshttps://doi.org/10.1016/j.compeleceng.2022.108326openalex-snapshot

TPSN: Transformer-based multi-Prototype Search Network for few-shot semantic segmentation

inferred← citeshttps://doi.org/10.1016/j.compeleceng.2024.109527openalex-snapshot

Adaptive similarity-guided self-merging network for few-shot semantic segmentation

inferred← citeshttps://doi.org/10.1016/j.compmedimag.2025.102505openalex-snapshot

CGNet: Few-shot learning for Intracranial Hemorrhage Segmentation

inferred← citeshttps://doi.org/10.1016/j.cviu.2023.103746openalex-snapshot

POEM: A prototype cross and emphasis network for few-shot semantic segmentation

inferred← citeshttps://doi.org/10.1016/j.cviu.2023.103860openalex-snapshot

CT-VOS: Cutout prediction and tagging for self-supervised video object segmentation

inferred← citeshttps://doi.org/10.1016/j.cviu.2024.104191openalex-snapshot

Found missing semantics: Supplemental prototype network for few-shot semantic segmentation

inferred← citeshttps://doi.org/10.1016/j.cviu.2025.104419openalex-snapshot

MSCA: A few-shot segmentation framework driven by multi-scale cross-attention and information extraction

inferred← citeshttps://doi.org/10.1016/j.displa.2025.103179openalex-snapshot

Exploiting independent query information for few-shot image segmentation

inferred← citeshttps://doi.org/10.1016/j.engappai.2023.106797openalex-snapshot

Query-support semantic correlation mining for few-shot segmentation

inferred← citeshttps://doi.org/10.1016/j.engappai.2024.109309openalex-snapshot

Cycle association prototype network for few-shot semantic segmentation

inferred← citeshttps://doi.org/10.1016/j.engappai.2025.112408openalex-snapshot

A novel prior knowledge-based and texture-enhanced network for few-shot industrial defect segmentation

inferred← citeshttps://doi.org/10.1016/j.eswa.2023.120382openalex-snapshot

Few-shot incremental learning in the context of solar cell quality inspection

inferred← citeshttps://doi.org/10.1016/j.eswa.2024.125377openalex-snapshot

Combining hierarchical sparse representation with adaptive prompt for few-shot segmentation

inferred← citeshttps://doi.org/10.1016/j.eswa.2024.125552openalex-snapshot

On filling the intra-class and inter-class gaps for few-shot segmentation

inferred← citeshttps://doi.org/10.1016/j.eswa.2025.130892openalex-snapshot

CLIP-driven feature disambiguation and cross-modal synergy for few-shot semantic segmentation

inferred← citeshttps://doi.org/10.1016/j.eswa.2026.132972openalex-snapshot

VLAlignSeg: Vision-language alignment for few-shot medical image segmentation

inferred← citeshttps://doi.org/10.1016/j.eswa.2026.134278openalex-snapshot

Decoupling semantic identity and structural boundaries for few-shot remote sensing segmentation

inferred← citeshttps://doi.org/10.1016/j.image.2024.117186openalex-snapshot

Prototype-wise self-knowledge distillation for few-shot segmentation

inferred← citeshttps://doi.org/10.1016/j.imavis.2024.105113openalex-snapshot

An enhanced approach for few-shot segmentation via smooth downsampling mask and label smoothing loss

inferred← citeshttps://doi.org/10.1016/j.imavis.2024.105214openalex-snapshot

SAM-RSP: A new few-shot segmentation method based on segment anything model and rough segmentation prompts

inferred← citeshttps://doi.org/10.1016/j.imavis.2025.105456openalex-snapshot

ESDA: Zero-shot semantic segmentation based on an embedding semantic space distribution adjustment strategy

inferred← citeshttps://doi.org/10.1016/j.imavis.2025.105672openalex-snapshot

MSDNet: Multi-scale decoder for few-shot semantic segmentation via transformer-guided prototyping

inferred← citeshttps://doi.org/10.1016/j.inffus.2024.102407openalex-snapshot

ExplainLFS: Explaining neural architectures for similarity learning from local perturbations in the latent feature space

inferred← citeshttps://doi.org/10.1016/j.infrared.2023.104718openalex-snapshot

Memory linked knowledge domain transfer few-shot learning for thermography nondestructive evaluation system

inferred← citeshttps://doi.org/10.1016/j.ins.2020.01.016openalex-snapshot

Recognizing novel patterns via adversarial learning for one-shot semantic segmentation

inferred← citeshttps://doi.org/10.1016/j.jii.2023.100544openalex-snapshot

A novel deep learning method for on-line monitoring of grate bar defects in sintering machine system

inferred← citeshttps://doi.org/10.1016/j.jvcir.2021.103306openalex-snapshot

Visible and thermal images fusion architecture for few-shot semantic segmentation

inferred← citeshttps://doi.org/10.1016/j.jvcir.2023.103754openalex-snapshot

Hierarchical context-agnostic network with contrastive feature diversity for one-shot semantic segmentation

inferred← citeshttps://doi.org/10.1016/j.jvcir.2023.103946openalex-snapshot

MFS enhanced SAM: Achieving superior performance in bimodal few-shot segmentation

inferred← citeshttps://doi.org/10.1016/j.knosys.2020.106647openalex-snapshot

FSS-2019-nCov: A deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection

inferred← citeshttps://doi.org/10.1016/j.knosys.2022.109508openalex-snapshot

Tackling background ambiguities in multi-class few-shot point cloud semantic segmentation

inferred← citeshttps://doi.org/10.1016/j.knosys.2024.112004openalex-snapshot

Learning robust correlation with foundation model for weakly-supervised few-shot segmentation

inferred← citeshttps://doi.org/10.1016/j.knosys.2024.112502openalex-snapshot

Activation function optimization scheme for image classification

inferred← citeshttps://doi.org/10.1016/j.knosys.2025.113149openalex-snapshot

LLM Knowledge-Driven Target Prototype Learning for Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1016/j.knosys.2025.113218openalex-snapshot

Learning prototypes from background and latent objects for few-shot semantic segmentation

inferred← citeshttps://doi.org/10.1016/j.knosys.2025.114606openalex-snapshot

Enhancing feature interaction for improved generalization in few-shot metal surface defect segmentation

inferred← citeshttps://doi.org/10.1016/j.media.2019.101587openalex-snapshot

'Squeeze & excite' guided few-shot segmentation of volumetric images.

inferred← citeshttps://doi.org/10.1016/j.media.2021.102170openalex-snapshot

Multi-level semantic adaptation for few-shot segmentation on cardiac image sequences

inferred← citeshttps://doi.org/10.1016/j.media.2022.102385openalex-snapshot

Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels

inferred← citeshttps://doi.org/10.1016/j.media.2023.102834openalex-snapshot

Learning what and where to segment: A new perspective on medical image few-shot segmentation

inferred← citeshttps://doi.org/10.1016/j.media.2023.102920openalex-snapshot

Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey

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