Few-Shot Learning

CVPR 2024arXiv:2403.06495

#2

Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts

Jiawen Zhu, Guansong Pang

92

AAAI 2024arXiv:2307.16424

#3

MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning

Baoquan Zhang, Chuyao Luo, Demin Yu et al.

meta-learningfew-shot learningdiffusion modelsgradient-based optimization+4

79

AAAI 2024arXiv:2304.01917

#4

Few-Shot Object Detection with Foundation Models

Guangxing Han, Ser-Nam Lim

Strong Baselines for Parameter-Efficient Few-Shot Fine-Tuning

Samyadeep Basu, Shell Hu, Daniela Massiceti et al.

few-shot classificationvision transformer fine-tuningparameter efficient fine-tuninglayer norm tuning+4

50

CVPR 2024arXiv:2404.02117

#6

Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners

Keon Hee Park, Kyungwoo Song, Gyeong-Moon Park

48

ICML 2025arXiv:2411.07279

#7

The Surprising Effectiveness of Test-Time Training for Few-Shot Learning

Ekin Akyürek, Mehul Damani, Adam Zweiger et al.

45

AAAI 2024arXiv:2401.07629

#8

Fine-Grained Prototypes Distillation for Few-Shot Object Detection

Zichen Wang, Bo Yang, Haonan Yue et al.

few-shot object detectionmeta-learningfeature aggregationprototype distillation+4

44

ICML 2024arXiv:2402.05403

#9

In-Context Principle Learning from Mistakes

Tianjun Zhang, Aman Madaan, Luyu Gao et al.

in-context learningfew-shot promptingmulti-hop question answeringtextual question answering+4

40

CVPR 2024arXiv:2311.18649

#10

Simple Semantic-Aided Few-Shot Learning

Hai Zhang, Junzhe Xu, Shanlin Jiang et al.

34

CVPR 2024arXiv:2405.18437

#11

Transductive Zero-Shot and Few-Shot CLIP

Ségolène Martin, Yunshi HUANG, Fereshteh Shakeri et al.

transductive inferencezero-shot classificationfew-shot classificationvision-language models+4

32

ICLR 2024arXiv:2402.11922

#12

Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation

Yuan Yuan, Chenyang Shao, Jingtao Ding et al.

31

CVPR 2024arXiv:2401.08407

#13

Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining

Jiahao Nie, Yun Xing, Gongjie Zhang et al.

31

CVPR 2024arXiv:2402.17614

#14

Adapt Before Comparison: A New Perspective on Cross-Domain Few-Shot Segmentation

Jonas Herzog

29

CVPR 2024arXiv:2404.08958

#15

AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learning

Yuwei Tang, ZhenYi Lin, Qilong Wang et al.

24

AAAI 2024arXiv:2401.01377

#16

Does Few-Shot Learning Suffer from Backdoor Attacks?

Xinwei Liu, Xiaojun Jia, Jindong Gu et al.

few-shot learningbackdoor attacksattack success ratebenign accuracy+4

24

ICML 2024arXiv:2405.17022

#17

Compositional Few-Shot Class-Incremental Learning

Yixiong Zou, Shanghang Zhang, haichen zhou et al.

few-shot learningclass-incremental learningcompositional learningprimitive composition+4

21

AAAI 2024arXiv:2303.05105

#18

MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentation

Authors: Minh-Quan Le, Tam Nguyen, Trung-Nghia Le et al.

20

ECCV 2024arXiv:2410.05627

#19

CLOSER: Towards Better Representation Learning for Few-Shot Class-Incremental Learning

Junghun Oh, Sungyong Baik, Kyoung Mu Lee

few-shot learningclass-incremental learningrepresentation learningcatastrophic forgetting+3

17

ECCV 2024arXiv:2405.19917

#20

Multimodal Cross-Domain Few-Shot Learning for Egocentric Action Recognition

Masashi Hatano, Ryo Hachiuma, Ryo Fujii et al.

17

CVPR 2025arXiv:2406.11148

#21

Few-Shot Recognition via Stage-Wise Retrieval-Augmented Finetuning

Tian Liu, Huixin Zhang, Shubham Parashar et al.

15

few-shot anomaly detectionvision-language modelprompt learningone-for-all paradigm+3

#22

One-for-All Few-Shot Anomaly Detection via Instance-Induced Prompt Learning

Wenxi Lv, Qinliang Su, Wenchao Xu

ICLR 2025

15

CVPR 2024arXiv:2403.02649

#23

Few-shot Learner Parameterization by Diffusion Time-steps

Zhongqi Yue, Pan Zhou, Richang Hong et al.

14

ICLR 2025arXiv:2502.00330

#24

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation

Xingchen Wan, Han Zhou, Ruoxi Sun et al.

14

AAAI 2025arXiv:2408.12469

#25

Envisioning Class Entity Reasoning by Large Language Models for Few-shot Learning

Mushui Liu, Fangtai Wu, Bozheng Li et al.

few-shot learningclass entity reasoninglarge language modelssemantic-guided visual extraction+3

13

ECCV 2024arXiv:2409.02882

#26

Benchmarking Spurious Bias in Few-Shot Image Classifiers

Guangtao Zheng, Wenqian Ye, Aidong Zhang

12

CVPR 2024arXiv:2403.04492

#27

Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot Learning

Rashindrie Perera, Saman Halgamuge

12

CVPR 2025arXiv:2503.11609

#28

DeIL: Direct-and-Inverse CLIP for Open-World Few-Shot Learning

Shuai Shao, Yu Bai, Yan WANG et al.

Rethinking Few-Shot Adaptation of Vision-Language Models in Two Stages

Matteo Farina, Massimiliano Mancini, Giovanni Iacca et al.

11

ICLR 2025arXiv:2506.17307

#30

Learning to Adapt Frozen CLIP for Few-Shot Test-Time Domain Adaptation

Zhixiang Chi, Li Gu, Huan Liu et al.

few-shot adaptationtest-time domain adaptationdomain shiftfrozen clip+4

10

ICLR 2024arXiv:2402.02444

#31

Self-Training Based Few-Shot Node Classification by Knowledge Distillation

Zongqian Wu, Yujie Mo, Peng Zhou et al.

BECLR: Batch Enhanced Contrastive Few-Shot Learning

Stylianos Poulakakis-Daktylidis, Hadi Jamali-Rad

9

ICML 2025arXiv:2501.08763

#33

Few-Shot Learner Generalizes Across AI-Generated Image Detection

Shiyu Wu, Jing Liu, Jing Li et al.

8

AAAI 2024arXiv:2312.11260

#34

A Dynamic Learning Method towards Realistic Compositional Zero-Shot Learning

Xiaoming Hu, Zilei Wang

Leveraging Normalization Layer in Adapters with Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot Learning

YongJin Yang, Taehyeon Kim, Se-Young Yun

cross-domain few-shot learningnormalization layer adaptersprogressive learningadaptive distillation+4

8

AAAI 2024arXiv:2405.03565

#36

Adversarially Robust Few-shot Learning via Parameter Co-distillation of Similarity and Class Concept Learners

Junhao Dong, Piotr Koniusz, Junxi Chen et al.

Liberating Seen Classes: Boosting Few-Shot and Zero-Shot Text Classification via Anchor

Han Liu, Siyang Zhao, Xiaotong Zhang et al.

few-shot text classificationzero-shot text classificationpre-trained language modelspseudo sample generation+4

7

ICLR 2024arXiv:2306.09295

#38

Neural Fine-Tuning Search for Few-Shot Learning

Panagiotis Eustratiadis, Łukasz Dudziak, Da Li et al.

7

AAAI 2025arXiv:2408.11297

#39

Self-Prompt Mechanism for Few-Shot Image Recognition

Mingchen Song, Huiqiang Wang, Guoqiang Zhong

Making Large Vision Language Models to Be Good Few-Shot Learners

Fan Liu, Wenwen Cai, Jian Huo et al.

few-shot classificationvision language modelsmeta-learning strategyinstruction fine-tuning+4

6

AAAI 2025arXiv:2401.16412

#41

Learning to Manipulate Under Limited Information

Wesley H. Holliday, Alexander Kristoffersen, Eric Pacuit

6

few-shot object detectionmulti-task learninggradient balancingknowledge distillation+3

#42

Adaptive Multi-task Learning for Few-shot Object Detection

Yan Ren, Yanling Li, Wai-Kin Adams Kong

ECCV 2024

6

ICML 2025arXiv:2506.08607

#43

Sample Efficient Demonstration Selection for In-Context Learning

Kiran Purohit, Venktesh V, Sourangshu Bhattacharya et al.

ECCV 2024arXiv:2409.17439

#44

Rejection Sampling IMLE: Designing Priors for Better Few-Shot Image Synthesis

Chirag Vashist, Shichong Peng, Ke Li

AAAI 2025arXiv:2502.20596

#45

Few-Shot, No Problem: Descriptive Continual Relation Extraction

Nguyen Xuan Thanh, Anh Duc Le, Quyen Tran et al.

continual relation extractionfew-shot learninglarge language modelsbi-encoder retrieval+3

CVPR 2025arXiv:2505.16778

#46

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching

Xianing Chen, Si Huo, Borui Jiang et al.

few-shot countingsingle domain generalizationvision-language representationscorrelation map construction+3

ECCV 2024arXiv:2409.19210

#47

Learning to Obstruct Few-Shot Image Classification over Restricted Classes

Amber Yijia Zheng, Chiao-An Yang, Raymond Yeh

few-shot classificationimage classificationattribute classificationmeta-learning+4

ECCV 2024arXiv:2404.19401

#48

UniFS: Universal Few-shot Instance Perception with Point Representations

Sheng Jin, Ruijie Yao, Lumin Xu et al.

few-shot learninginstance segmentationobject detectionpoint representation learning+3

ICML 2024arXiv:2305.14521

#49

Few-shot Adaptation to Distribution Shifts By Mixing Source and Target Embeddings

Yihao Xue, Ali Payani, Yu Yang et al.

few-shot adaptationdistribution shiftsembedding mixinglinear classifier+3

NEURIPS 2025arXiv:2509.25033

#50

VT-FSL: Bridging Vision and Text with LLMs for Few-Shot Learning

Wenhao Li, Qiangchang Wang, Xianjing Meng et al.

few-shot learningcross-modal promptingvision-language modelsmultimodal alignment+4

NEURIPS 2025arXiv:2504.06792

#51

Domain-Specific Pruning of Large Mixture-of-Experts Models with Few-shot Demonstrations

Zican Dong, Han Peng, Peiyu Liu et al.

AAAI 2024arXiv:2312.13495

#52

Scaling Few-Shot Learning for the Open World

Zhipeng Lin, Wenjing Yang, Haotian Wang et al.

Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling

Jie Han, Yixiong Zou, Haozhao Wang et al.

3

ICML 2024arXiv:2402.12706

#54

Learning Causal Domain-Invariant Temporal Dynamics for Few-Shot Action Recognition

Yuke Li, Guangyi Chen, Ben Abramowitz et al.

few-shot action recognitioncausal representation learningtemporal dynamicsdomain-invariant representations+3

3

ICML 2025arXiv:2505.24190

#55

Provably Improving Generalization of Few-shot models with Synthetic Data

Lan-Cuong Nguyen, Quan Nguyen-Tri, Bang Khanh et al.

3

ICML 2025arXiv:2506.06033

#56

Large Language Models are Demonstration Pre-Selectors for Themselves

Jiarui Jin, Yuwei Wu, Haoxuan Li et al.

CVPR 2025arXiv:2506.09473

#57

SEC-Prompt:SEmantic Complementary Prompting for Few-Shot Class-Incremental Learning

Ye Liu, Meng Yang

Provoking Multi-modal Few-Shot LVLM via Exploration-Exploitation In-Context Learning

Cheng Chen, Yunpeng Zhai, Yifan Zhao et al.

in-context learninglarge vision-language modelsmulti-modal demonstration selectionexploration-exploitation reinforcement learning+3

ICCV 2025arXiv:2509.07917

#59

Pseudo Informative Episode Construction for Few-Shot Class-Incremental Learning

Chaofan Chen, Xiaoshan Yang, Changsheng Xu

Object-level Correlation for Few-Shot Segmentation

chunlin wen, Yu Zhang, Jie Fan et al.

few-shot segmentationsemantic segmentationobject-level correlationsupport target object+3

CVPR 2025arXiv:2506.15720

#61

Tripartite Weight-Space Ensemble for Few-Shot Class-Incremental Learning

Juntae Lee, Munawar Hayat, Sungrack Yun

few-shot learningclass-incremental learningcatastrophic forgettingweight-space ensemble+3

NEURIPS 2025arXiv:2510.21631

#62

Unlocking the Potential of Black-box Pre-trained GNNs for Graph Few-shot Learning

Qiannan Zhang, Shichao Pei, Yuan Fang et al.

Few-Shot Knowledge Distillation of LLMs With Counterfactual Explanations

Faisal Hamman, Pasan Dissanayake, Yanjun Fu et al.

knowledge distillationfew-shot learningcounterfactual explanationslarge language models+3

ICCV 2025arXiv:2411.18651

#64

Verbalized Representation Learning for Interpretable Few-Shot Generalization

Cheng-Fu Yang, Da Yin, Wenbo Hu et al.

vision-language modelsfew-shot learningverbalized representation learninginterpretable features+3

CVPR 2025arXiv:2506.03117

#65

Targeted Forgetting of Image Subgroups in CLIP Models

Zeliang Zhang, Gaowen Liu, Charles Fleming et al.

NEURIPS 2025arXiv:2510.23013

#66

MoEMeta: Mixture-of-Experts Meta Learning for Few-Shot Relational Learning

Han Wu, Jie Yin

few-shot learningknowledge graph reasoningrelational learningmixture-of-experts models+4

AAAI 2025arXiv:2402.18292

#67

FSL-Rectifier: Rectify Outliers in Few-Shot Learning via Test-Time Augmentation

Yunwei Bai, Ying Kiat Tan, Shiming Chen et al.

CVPR 2025arXiv:2412.16739

#68

Attraction Diminishing and Distributing for Few-Shot Class-Incremental Learning

Li-Jun Zhao, Zhen-Duo Chen, Yongxin Wang et al.

UNEM: UNrolled Generalized EM for Transductive Few-Shot Learning

Long Zhou, Fereshteh Shakeri, Aymen Sadraoui et al.

few-shot learningclass-incremental learningapproximation risk analysistransfer risk+4

#70

On the Approximation Risk of Few-Shot Class-Incremental Learning

Xuan Wang, Zhong Ji, Xiyao Liu et al.

ECCV 2024

NEURIPS 2025arXiv:2505.19107

#71

Optimization Inspired Few-Shot Adaptation for Large Language Models

Boyan Gao, Xin Wang, Yibo Yang et al.

few-shot adaptationlarge language modelsparameter-efficient fine-tuningin-context learning+3

ICCV 2025arXiv:2507.07994

#72

Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection

Subhajit Maity, Ayan Bhunia, Subhadeep Koley et al.

keypoint detectionfew-shot learningsketch-based learningcross-modal embeddings+2

ICML 2025arXiv:2505.07921

#73

Pushing the Limit of Fine-Tuning for Few-Shot Learning: Where Feature Reusing Meets Cross-Scale Attention

Ying-Yu Chen, Jun-Wei Hsieh, Xin Li et al.

Unknown Text Learning for CLIP-based Few-Shot Open-set Recognition

Rui Ma, Qilong Wang, Bing Cao et al.

ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning

Haoyuan Yang, Xiaoou Li, Jiaming Lv et al.

M2SD:Multiple Mixing Self-Distillation for Few-Shot Class-Incremental Learning

Jinhao Lin, Ziheng Wu, Weifeng Lin et al.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning

Qi Xu, Junyang Zhu, Dongdong Zhou et al.

NEURIPS 2025arXiv:2510.12140

#78

Task-Adaptive Prompted Transformer for Cross-Domain Few-Shot Learning

Jiamin Wu, Xin Liu, Xiaotian Yin et al.

Optimal Transport of Diverse Unsupervised Tasks for Robust Learning from Noisy Few-Shot Data

Xiaofan Que, Qi Yu

FedFSL-CFRD: Personalized Federated Few-Shot Learning with Collaborative Feature Representation Disentanglement

Shanfeng Wang, Jianzhao Li, Zaitian Liu et al.

Graph Few-Shot Learning via Adaptive Spectrum Experts and Cross-Set Distribution Calibration

Yonghao Liu, Yajun Wang, Chunli Guo et al.

ICCV 2025arXiv:2508.17636

#82

Flexi-FSCIL: Adaptive Knowledge Retention for Breaking the Stability-Plasticity Dilemma in Few-Shot Class-Incremental Learning

Wufei Xie, Yalin Wang, Chenliang Liu et al.

Few-Shot Pattern Detection via Template Matching and Regression

Eunchan Jo, Dahyun Kang, Sanghyun Kim et al.

ICCV 2025arXiv:2509.13185

#84

Is Meta-Learning Out? Rethinking Unsupervised Few-Shot Classification with Limited Entropy

Yunchuan Guan, Yu Liu, Ke Zhou et al.

ICCV 2025arXiv:2507.20834

#85

ArtEditor: Learning Customized Instructional Image Editor from Few-Shot Examples

Shijie Huang, Yiren Song, Yuxuan Zhang et al.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting

Alexey Kravets, Da Chen, Vinay Namboodiri

federated learningfew-shot learningclass-incremental learningcatastrophic forgetting+4

#87

Few-Shot Incremental Learning via Foreground Aggregation and Knowledge Transfer for Audio-Visual Semantic Segmentation

Jingqiao Xiu, Mengze Li, Zongxin Yang et al.

Collaborative Consortium of Foundation Models for Open-World Few-Shot Learning

Shuai Shao, Yu Bai, Yan Wang et al.

Task-Specific Gradient Adaptation for Few-Shot One-Class Classification

Yunlong Li, Xiabi Liu, Liyuan Pan et al.

Federated Few-Shot Class-Incremental Learning

Muhammad Anwar Masum, Mahardhika Pratama, Lin Liu et al.

ICLR 2025

COLM 2025arXiv:2507.16217

#91

Towards Effective Foundation Model Adaptation for Extreme Cross-Domain Few-Shot Learning

Fei Zhou, Peng Wang, Lei Zhang et al.

DeFSS: Image-to-Mask Denoising Learning for Few-shot Segmentation

Zishu Qin, Junhao Xu, Weifeng Ge

Dual-Level Curriculum Meta-Learning for Noisy Few-Shot Learning Tasks

Xiaofan Que, Qi Yu

VERO: Verification and Zero-Shot Feedback Acquisition for Few-Shot Multimodal Aspect-Level Sentiment Classification

Kai Sun, Hao Wu, Bin Shi et al.

Towards Compute-Optimal Many-Shot In-Context Learning

Shahriar Golchin, Yanfei Chen, Rujun Han et al.

Many-Shot In-Context LearningIn-Context LearningLarge Language ModelsCompute-Optimal In-Context Learning+1