Cover Image

Semantic-Anchored, Class Variance-Optimized Clustering for Robust Semi-Supervised Few-Shot Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Maji, Souvik, Baghel, Rhythm, Mazumder, Pratik
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning
by: Lyu, Fan, et al.
Published: (2025)

A Strong Baseline for Semi-Supervised Incremental Few-Shot Learning
by: Zhao, Linglan, et al.
Published: (2021)

GCT: Graph Co-Training for Semi-Supervised Few-Shot Learning
by: Xu, Rui, et al.
Published: (2022)

Semi-Supervised Few-Shot Adaptation of Vision-Language Models
by: Silva-Rodríguez, Julio, et al.
Published: (2026)

SPARSE Data, Rich Results: Few-Shot Semi-Supervised Learning via Class-Conditioned Image Translation
by: Manni, Guido, et al.
Published: (2025)

Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot Learning
by: Dong, Xingping, et al.
Published: (2022)

Semantic Class Distribution Learning for Debiasing Semi-Supervised Medical Image Segmentation
by: Su, Yingxue, et al.
Published: (2026)

Learning Robust Correlation with Foundation Model for Weakly-Supervised Few-Shot Segmentation
by: Huang, Xinyang, et al.
Published: (2024)

Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization
by: Li, Mengtian, et al.
Published: (2024)

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective
by: Liu, Tian, et al.
Published: (2025)

Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification
by: Xiao, Yuyang
Published: (2024)

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals
by: Zakir, Hussni Mohd, et al.
Published: (2024)

SPRINT: Semi-supervised Prototypical Representation for Few-Shot Class-Incremental Tabular Learning
by: Suleymanov, Umid, et al.
Published: (2026)

Bias Mitigating Few-Shot Class-Incremental Learning
by: Zhao, Li-Jun, et al.
Published: (2024)

Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts
by: De Marinis, Pasquale, et al.
Published: (2024)

Simple Semantic-Aided Few-Shot Learning
by: Zhang, Hai, et al.
Published: (2023)

MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning
by: Wang, Zhaolong, et al.
Published: (2025)

Bridging the Semantic Chasm: Synergistic Conceptual Anchoring for Generalized Few-Shot and Zero-Shot OOD Perception
by: Christoforos, Alexandros, et al.
Published: (2026)

Few-Shot Learning with Class Imbalance
by: Ochal, Mateusz, et al.
Published: (2021)

Adaptive Decision Boundary for Few-Shot Class-Incremental Learning
by: Li, Linhao, et al.
Published: (2025)

Controllable Relation Disentanglement for Few-Shot Class-Incremental Learning
by: Zhou, Yuan, et al.
Published: (2024)

Active Self-Semi-Supervised Learning for Few Labeled Samples
by: Wen, Ziting, et al.
Published: (2022)

CALA: A Class-Aware Logit Adapter for Few-Shot Class-Incremental Learning
by: Liu, Chengyan, et al.
Published: (2024)

Compositional Few-Shot Class-Incremental Learning
by: Zou, Yixiong, et al.
Published: (2024)

LEDetection: A Simple Framework for Semi-Supervised Few-Shot Object Detection
by: Tran, Phi Vu
Published: (2023)

Singular Value Fine-tuning for Few-Shot Class-Incremental Learning
by: Wang, Zhiwu, et al.
Published: (2025)

Enhanced Few-Shot Class-Incremental Learning via Ensemble Models
by: Zhu, Mingli, et al.
Published: (2024)

Learning to Obstruct Few-Shot Image Classification over Restricted Classes
by: Zheng, Amber Yijia, et al.
Published: (2024)

Towards Non-Exemplar Semi-Supervised Class-Incremental Learning
by: Liu, Wenzhuo, et al.
Published: (2024)

Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision
by: Gaillochet, Mélanie, et al.
Published: (2024)

CDMAD: Class-Distribution-Mismatch-Aware Debiasing for Class-Imbalanced Semi-Supervised Learning
by: Lee, Hyuck, et al.
Published: (2024)

Adaptive Sample-Level Framework Motivated by Distributionally Robust Optimization with Variance-Based Radius Assignment for Enhanced Neural Network Generalization Under Distribution Shift
by: Sravon, Aheer, et al.
Published: (2025)

Zero-Shot Pseudo Labels Generation Using SAM and CLIP for Semi-Supervised Semantic Segmentation
by: Saito, Nagito, et al.
Published: (2025)

Learning Prompt with Distribution-Based Feature Replay for Few-Shot Class-Incremental Learning
by: Huang, Zitong, et al.
Published: (2024)

Few-Shot Class-Incremental Learning For Efficient SAR Automatic Target Recognition
by: Karantaidis, George, et al.
Published: (2025)

PL-FSCIL: Harnessing the Power of Prompts for Few-Shot Class-Incremental Learning
by: Tian, Songsong, et al.
Published: (2024)

Prototype Optimization with Neural ODE for Few-Shot Learning
by: Zhang, Baoquan, et al.
Published: (2024)

SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learning
by: Lai, Jinxiang, et al.
Published: (2023)

tSF: Transformer-based Semantic Filter for Few-Shot Learning
by: Lai, Jinxiang, et al.
Published: (2022)

Spectral-Spatial Self-Supervised Learning for Few-Shot Hyperspectral Image Classification
by: Chen, Wenchen, et al.
Published: (2025)