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Few-Shot Class-Incremental Learning

3개 벤치마크 · 논문 143편 · 이 태스크의 논문 보기 →

Benchmarks

mini-Imagenet

결과 25개

CIFAR-100

결과 22개

CUB-200-2011

결과 12개

Most implemented

Continuous Knowledge-Preserving Decomposition for Few-Shot Continual Learning

2025-01-09 · 구현 2개

A streamlined Approach to Multimodal Few-Shot Class Incremental Learning for Fine-Grained Datasets

2024-03-10 · 구현 2개

FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language Models

2023-12-28 · 구현 2개

Continual Learning: Forget-free Winning Subnetworks for Video Representations

2023-12-19 · 구현 2개

Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants

2023-08-03 · 구현 2개

On the Soft-Subnetwork for Few-shot Class Incremental Learning

2022-09-15 · 구현 2개

Papers

Adapter-Based Few-Shot Continual Learning for Malicious Packet Recognition

2026-08-24 · Kyle Stein, Guillermo Francia, III Eman El-Sheikh, Andrew Arash Mahyari arxiv

The continual evolution of malware variants necessitates detection systems that can adapt to new threats without retraining from scratch. However, continually updating models on new data often leads to catastrophic forge…

Few-Shot Class-Incremental LearningSelf-Supervised LearningMalware ClassificationContinual Learning

Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning

2026-06-03 · Fan Zhang, Sijin Zheng, Fei Ma, Qiang Yin 외 arxiv

Few-shot class-incremental learning (FSCIL) in synthetic aperture radar imagery presents unique challenges due to severe data scarcity and SAR-specific variability. In particular, strong azimuth sensitivity in SAR induce…

Few-Shot Class-Incremental LearningClass Incremental Learning

HyCal: A Training-Free Prototype Calibration Method for Cross-Discipline Few-Shot Class-Incremental Learning

2026-04-17 · Eunju Lee, MiHyeon Kim, JuneHyoung Kwon, Yoonji Lee 외 arxiv

Pretrained Vision-Language Models (VLMs) like CLIP show promise in continual learning, but existing Few-Shot Class-Incremental Learning (FSCIL) methods assume homogeneous domains and balanced data distributions, limiting…

Few-Shot Class-Incremental LearningContinual Learning

When Sensing Varies with Contexts: Context-as-Transform for Tactile Few-Shot Class-Incremental Learning

2026-03-26 · Yifeng Lin, Aiping Huang, Wenxi Liu, Si Wu 외 arxiv

Few-Shot Class-Incremental Learning (FSCIL) can be particularly susceptible to acquisition contexts with only a few labeled samples. A typical scenario is tactile sensing, where the acquisition context ({\it e.g.}, diver…

Few-Shot Class-Incremental Learning

SPRINT: Semi-supervised Prototypical Representation for Few-Shot Class-Incremental Tabular Learning

2026-03-04 · Umid Suleymanov, Murat Kantarcioglu, Kevin S Chan, Michael De Lucia 외 arxiv

Real-world systems must continuously adapt to novel concepts from limited data without forgetting previously acquired knowledge. While Few-Shot Class-Incremental Learning (FSCIL) is established in computer vision, its ap…

Few-Shot Class-Incremental Learning

Unlocking Prototype Potential: An Efficient Tuning Framework for Few-Shot Class-Incremental Learning

2026-02-05 · Shengqin Jiang, Xiaoran Feng, Yuankai Qi, Haokui Zhang 외 arxiv

Few-shot class-incremental learning (FSCIL) seeks to continuously learn new classes from very limited samples while preserving previously acquired knowledge. Traditional methods often utilize a frozen pre-trained feature…

Few-Shot Class-Incremental Learning

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