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Harmonic Feature Activation for Few-Shot Semantic Segmentation

https://doi.org/10.1109/tip.2021.3058512

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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-030-58545-7_9openalex-snapshot

Part-Aware Prototype Network for Few-Shot Semantic Segmentation

inferredcites →https://doi.org/10.1007/978-3-030-58598-3_45openalex-snapshot

Prototype Mixture Models for Few-Shot Semantic Segmentation

inferredcites →https://doi.org/10.1007/978-3-030-58607-2_45openalex-snapshot

Selecting Relevant Features from a Multi-domain Representation for Few-Shot Classification

inferredcites →https://doi.org/10.1007/978-3-030-61609-0_54openalex-snapshot

Bilinear Models for Machine Learning

inferredcites →https://doi.org/10.1007/978-3-319-10602-1_48openalex-snapshot

Microsoft COCO: Common Objects in Context

inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot

U-Net: Convolutional Networks for Biomedical Image Segmentation

inferredcites →https://doi.org/10.1007/978-3-319-46466-4_37openalex-snapshot

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

inferredcites →https://doi.org/10.1007/s11263-009-0275-4openalex-snapshot

The Pascal Visual Object Classes (VOC) Challenge

inferredcites →https://doi.org/10.1016/j.neucom.2019.05.104openalex-snapshot

Compact bilinear pooling via kernelized random projection for fine-grained image categorization on low computational power devices

inferredcites →https://doi.org/10.1016/j.neucom.2019.12.034openalex-snapshot

Meta-Learning based prototype-relation network for few-shot classification

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.565openalex-snapshot

One-Shot Video Object Segmentation

inferredcites →https://doi.org/10.1109/cvpr.2017.660openalex-snapshot

Pyramid Scene Parsing Network

inferredcites →https://doi.org/10.1109/cvpr.2018.00760openalex-snapshot

Low-Shot Learning from Imaginary Data

inferredcites →https://doi.org/10.1109/cvpr.2019.00536openalex-snapshot

CANet: Class-Agnostic Segmentation Networks With Iterative Refinement and Attentive Few-Shot Learning

inferredcites →https://doi.org/10.1109/cvpr.2019.00641openalex-snapshot

Spot and Learn: A Maximum-Entropy Patch Sampler for Few-Shot Image Classification

inferredcites →https://doi.org/10.1109/cvpr.2019.00971openalex-snapshot

FEELVOS: Fast End-To-End Embedding Learning for Video Object Segmentation

inferredcites →https://doi.org/10.1109/cvpr.2019.01199openalex-snapshot

Task Agnostic Meta-Learning for Few-Shot Learning

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00160openalex-snapshot

HRank: Filter Pruning Using High-Rank Feature Map

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00422openalex-snapshot

CRNet: Cross-Reference Networks for Few-Shot Segmentation

inferredcites →https://doi.org/10.1109/cvpr42600.2020.01038openalex-snapshot

Memory Aggregation Networks for Efficient Interactive Video Object Segmentation

inferredcites →https://doi.org/10.1109/iccv.2011.6126343openalex-snapshot

Semantic contours from inverse detectors

inferredcites →https://doi.org/10.1109/iccv.2017.328openalex-snapshot

Low-Shot Visual Recognition by Shrinking and Hallucinating Features

inferredcites →https://doi.org/10.1109/iccv.2019.00071openalex-snapshot

Feature Weighting and Boosting for Few-Shot Segmentation

inferredcites →https://doi.org/10.1109/iccv.2019.00855openalex-snapshot

Collect and Select: Semantic Alignment Metric Learning for Few-Shot Learning

inferredcites →https://doi.org/10.1109/iccv.2019.00929openalex-snapshot

PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment

inferredcites →https://doi.org/10.1109/iccv.2019.00968openalex-snapshot

Pyramid Graph Networks With Connection Attentions for Region-Based One-Shot Semantic Segmentation

inferredcites →https://doi.org/10.1109/tcyb.2020.2992433openalex-snapshot

SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation

inferredcites →https://doi.org/10.1109/tip.2016.2552401openalex-snapshot

Ontology-Based Semantic Image Segmentation Using Mixture Models and Multiple CRFs

inferredcites →https://doi.org/10.1109/tip.2018.2861573openalex-snapshot

A Unified Approach for Conventional Zero-Shot, Generalized Zero-Shot, and Few-Shot Learning

inferredcites →https://doi.org/10.1109/tip.2019.2926748openalex-snapshot

Coarse-to-Fine Semantic Segmentation From Image-Level Labels

inferredcites →https://doi.org/10.1109/tip.2019.2959254openalex-snapshot

A Two-Stage Approach to Few-Shot Learning for Image Recognition

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/10.1109/tpami.2018.2844175openalex-snapshot

Mask R-CNN

inferredcites →https://doi.org/10.1137/07070111xopenalex-snapshot

Tensor Decompositions and Applications

inferredcites →https://doi.org/10.1609/aaai.v33i01.33018441openalex-snapshot

Attention-Based Multi-Context Guiding for Few-Shot Semantic 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.1412.7062openalex-snapshot

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

inferredcites →https://doi.org/10.48550/arxiv.1703.03400openalex-snapshot

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

inferredcites →https://doi.org/10.48550/arxiv.2003.13246openalex-snapshot

Memory Aggregation Networks for Efficient Interactive Video Object Segmentation

inferredcites →https://doi.org/10.5244/c.31.117openalex-snapshot

Improved Bilinear Pooling with CNNs

inferredcites →https://doi.org/10.5244/c.31.167openalex-snapshot

One-Shot Learning for Semantic Segmentation

What points at this work

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

inferred← citeshttps://doi.org/10.2139/ssrn.4815121openalex-extracted

Combination Learning for Few-Shot Segmentation

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

Exploring Hierarchical Prototypes for Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-032-31654-7_34openalex-snapshot

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

inferred← citeshttps://doi.org/10.1007/978-3-032-36984-0_26openalex-snapshot

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

inferred← citeshttps://doi.org/10.1007/s00138-023-01486-zopenalex-snapshot

Few-shot object detection via data augmentation and distribution calibration

inferred← citeshttps://doi.org/10.1007/s00371-024-03747-yopenalex-snapshot

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

inferred← citeshttps://doi.org/10.1007/s00521-023-08235-3openalex-snapshot

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

inferred← citeshttps://doi.org/10.1007/s00521-023-08758-9openalex-snapshot

Few-shot semantic segmentation: a review on recent approaches

inferred← citeshttps://doi.org/10.1007/s00530-024-01294-zopenalex-snapshot

Overcomplete-to-sparse representation learning for few-shot class-incremental learning

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

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

inferred← citeshttps://doi.org/10.1007/s10489-023-04922-9openalex-snapshot

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

inferred← citeshttps://doi.org/10.1007/s10489-023-04937-2openalex-snapshot

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

inferred← citeshttps://doi.org/10.1007/s10489-024-05582-zopenalex-snapshot

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

inferred← citeshttps://doi.org/10.1007/s11042-023-17553-6openalex-snapshot

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

inferred← citeshttps://doi.org/10.1007/s11063-024-11511-5openalex-snapshot

Few-Shot Semantic Segmentation via Mask Aggregation

inferred← citeshttps://doi.org/10.1016/j.asoc.2026.115974openalex-snapshot

Global and local feature fusion network for few-shot image generation with space generation

inferred← citeshttps://doi.org/10.1016/j.engappai.2026.114096openalex-snapshot

Enhancing surface defect detection in industrial products through few-shot learning via meta-learning

inferred← citeshttps://doi.org/10.1016/j.engappai.2026.116365openalex-snapshot

Holistic foreground–background guided feature learning for few-shot strip steel defect segmentation

inferred← citeshttps://doi.org/10.1016/j.eswa.2023.122695openalex-snapshot

Scale-pyramid dynamic atrous convolution for pixel-level labeling

inferred← citeshttps://doi.org/10.1016/j.eswa.2025.130906openalex-snapshot

APENet: Task-aware adaptation prototype evolution network for few-shot semantic segmentation

inferred← citeshttps://doi.org/10.1016/j.infrared.2022.104164openalex-snapshot

R-Net: A novel fully convolutional network–based infrared image segmentation method for intelligent human behavior analysis

inferred← citeshttps://doi.org/10.1016/j.ins.2022.08.048openalex-snapshot

Coarse-to-fine few-shot classification with deep metric learning

inferred← citeshttps://doi.org/10.1016/j.neucom.2024.129160openalex-snapshot

Bi-orientated rectification few-shot segmentation network based on fine-grained prototypes

inferred← citeshttps://doi.org/10.1016/j.neucom.2025.132001openalex-snapshot

Enhancing few-shot segmentation via mask combination learning

inferred← citeshttps://doi.org/10.1016/j.neunet.2024.106802openalex-snapshot

Uncertainty guided semi-supervised few-shot segmentation with prototype level fusion

inferred← citeshttps://doi.org/10.1109/access.2021.3118472openalex-snapshot

Pyramid Co-Attention Compare Network for Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1109/access.2025.3529812openalex-snapshot

Heavy and Lightweight Deep Learning Models for Semantic Segmentation: A Survey

inferred← citeshttps://doi.org/10.1109/bibm52615.2021.9669727openalex-snapshot

PoissonSeg: Semi-Supervised Few-Shot Medical Image Segmentation via Poisson Learning

inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.00529openalex-snapshot

Multiple Instance Active Learning for Object Detection

inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.00728openalex-snapshot

Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection

inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.00962openalex-snapshot

Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation

inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.00789openalex-snapshot

Learning What Not to Segment: A New Perspective on Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1109/cvpr52733.2024.00296openalex-snapshot

LLaFS: When Large Language Models Meet Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1109/icassp43922.2022.9746619openalex-snapshot

Dual-Attention Network for Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1109/icip55913.2025.11084286openalex-snapshot

Holism To Atomism: Enhancing The Vision-Language Alignment For Cross-Modal Few-Shot Learning

inferred← citeshttps://doi.org/10.1109/jas.2023.123207openalex-snapshot

Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview

inferred← citeshttps://doi.org/10.1109/ojcs.2025.3592291openalex-snapshot

Few-Shot Segmentation Using Multi-Similarity and Attention Guidance

inferred← citeshttps://doi.org/10.1109/tai.2024.3369553openalex-snapshot

A Dense Multicross Self-Attention and Adaptive Gated Perceptual Unit Method for Few-Shot Semantic Segmentation

inferred← citeshttps://doi.org/10.1109/tcsvt.2021.3125129openalex-snapshot

Learning to Capture the Query Distribution for Few-Shot Learning

inferred← citeshttps://doi.org/10.1109/tcsvt.2022.3223150openalex-snapshot

Quaternion-Valued Correlation Learning for Few-Shot Semantic Segmentation

inferred← citeshttps://doi.org/10.1109/tcsvt.2026.3705224openalex-snapshot

ReAlign: Re-Aligning Intra-Class Misalignment for Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1109/tgrs.2024.3519772openalex-snapshot

Embedding Generalized Semantic Knowledge Into Few-Shot Remote Sensing Segmentation

inferred← citeshttps://doi.org/10.1109/tii.2022.3216900openalex-snapshot

Unseen-Material Few-Shot Defect Segmentation With Optimal Bilateral Feature Transport Network

inferred← citeshttps://doi.org/10.1109/tip.2022.3228162openalex-snapshot

Efficient Few-Shot Object Detection via Knowledge Inheritance

inferred← citeshttps://doi.org/10.1109/tip.2023.3319274openalex-snapshot

Adaptive Betweenness Clustering for Semi-Supervised Domain Adaptation

inferred← citeshttps://doi.org/10.1109/tip.2024.3364056openalex-snapshot

Dual Branch Multi-Level Semantic Learning for Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1109/tip.2026.3652357openalex-snapshot

Toward Generative Understanding: Incremental Few-Shot Semantic Segmentation With Diffusion Models

inferred← citeshttps://doi.org/10.1109/tip.2026.3723708openalex-snapshot

UINO-FSS: Unifying Representation Learning and Few-Shot Segmentation via Hierarchical Distillation and Mamba-HyperCorrelation

inferred← citeshttps://doi.org/10.1109/tip.2026.3728632openalex-snapshot

Hierarchical Dense Aggregation Network for Few-Shot Metal Surface Defect Segmentation

inferred← citeshttps://doi.org/10.1109/tmm.2022.3174405openalex-snapshot

APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation

inferred← citeshttps://doi.org/10.1109/tnnls.2023.3269513openalex-snapshot

Coupling Global Context and Local Contents for Weakly-Supervised Semantic Segmentation

inferred← citeshttps://doi.org/10.1109/tnnls.2023.3331648openalex-snapshot

Proposal Distribution Calibration for Few-Shot Object Detection

inferred← citeshttps://doi.org/10.1109/tnnls.2025.3543620openalex-snapshot

SDSimPoint: Shallow-Deep Similarity Learning for Few-Shot Point Cloud Semantic Segmentation

inferred← citeshttps://doi.org/10.1109/tpami.2022.3175849openalex-snapshot

Dynamic Support Network for Few-Shot Class Incremental Learning

inferred← citeshttps://doi.org/10.1109/tpami.2022.3193587openalex-snapshot

Holistic Prototype Activation for Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1109/tpami.2023.3265865openalex-snapshot

Base and Meta: A New Perspective on Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1109/tpami.2023.3273291openalex-snapshot

Learnable Distribution Calibration for Few-Shot Class-Incremental Learning

inferred← citeshttps://doi.org/10.1109/tpami.2023.3285755openalex-snapshot

Few-Shot Multi-Agent Perception With Ranking-Based Feature Learning

inferred← citeshttps://doi.org/10.1109/tvcg.2026.3700582openalex-snapshot

SegPainter: User-Controllable Face Inpainting via Mask-Aware Semantic Segmentation-Guided Mamba

inferred← citeshttps://doi.org/10.1109/wacv61042.2026.00355openalex-snapshot

DOTGraph: CLIP-Driven Feature Disentanglement and Optimal Transport based Graph Learning for Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1145/3474085.3478873openalex-snapshot

Few-shot Learning for Multi-Modality Tasks

inferred← citeshttps://doi.org/10.1145/3503161.3548160openalex-snapshot

Semantics-Driven Generative Replay for Few-Shot Class Incremental Learning

inferred← citeshttps://doi.org/10.12677/csa.2024.142034openalex-snapshot

Few-Shot Semantic Segmentation Method Based on the Fusion of Multi-Scale Priors

inferred← citeshttps://doi.org/10.32604/cmes.2023.026745openalex-snapshot

Filter Bank Networks for Few-Shot Class-Incremental Learning

inferred← citeshttps://doi.org/10.3390/math14122059openalex-snapshot

BAFNet: A Few-Shot Segmentation Algorithm Based on Two-Stage Backbone Network Adaptation Fine-Tuning via Meta-Learning

inferred← citeshttps://doi.org/10.3390/s21227625openalex-snapshot

Fish Segmentation in Sonar Images by Mask R-CNN on Feature Maps of Conditional Random Fields

inferred← citeshttps://doi.org/10.3390/s24154975openalex-snapshot

A Self-Supervised Few-Shot Semantic Segmentation Method Based on Multi-Task Learning and Dense Attention Computation

inferred← citeshttps://doi.org/10.4018/ijwltt.333601openalex-snapshot

Application of Multimedia Data Feature Extraction Technology in Teaching Classical Oil Painting

inferred← citeshttps://doi.org/10.48550/arxiv.2104.02281openalex-snapshot

Learnable Expansion-and-Compression Network for Few-shot Class-Incremental Learning

inferred← citeshttps://doi.org/10.48550/arxiv.2106.00184openalex-snapshot

Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation

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