Few-Shot Learning
Learning from very few examples
Top Papers
MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning
Baoquan Zhang, Chuyao Luo, Demin Yu et al.
LLaFS: When Large Language Models Meet Few-Shot Segmentation
Lanyun Zhu, Tianrun Chen, Deyi Ji et al.
A Closer Look at the Few-Shot Adaptation of Large Vision-Language Models
Julio Silva-RodrΓguez, Sina Hajimiri, Ismail Ben Ayed et al.
Few-Shot Object Detection with Foundation Models
Guangxing Han, Ser-Nam Lim
Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners
Keon Hee Park, Kyungwoo Song, Gyeong-Moon Park
Fine-Grained Prototypes Distillation for Few-Shot Object Detection
Zichen Wang, Bo Yang, Haonan Yue et al.
ID-like Prompt Learning for Few-Shot Out-of-Distribution Detection
Yichen Bai, Zongbo Han, Bing Cao et al.
The Surprising Effectiveness of Test-Time Training for Few-Shot Learning
Ekin AkyΓΌrek, Mehul Damani, Adam Zweiger et al.
Simple Semantic-Aided Few-Shot Learning
Hai Zhang, Junzhe Xu, Shanlin Jiang et al.
Transductive Zero-Shot and Few-Shot CLIP
Ségolène Martin, Yunshi HUANG, Fereshteh Shakeri et al.
Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation
Yuan Yuan, Chenyang Shao, Jingtao Ding et al.
Adapt Before Comparison: A New Perspective on Cross-Domain Few-Shot Segmentation
Jonas Herzog
Does Few-Shot Learning Suffer from Backdoor Attacks?
Xinwei Liu, Xiaojun Jia, Jindong Gu et al.
Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning
Yixiong Zou, Yicong Liu, Yiman Hu et al.
Large Language Models are Good Prompt Learners for Low-Shot Image Classification
Zhaoheng Zheng, Jingmin Wei, Xuefeng Hu et al.
CLOSER: Towards Better Representation Learning for Few-Shot Class-Incremental Learning
Junghun Oh, Sungyong Baik, Kyoung Mu Lee
FrugalNeRF: Fast Convergence for Extreme Few-shot Novel View Synthesis without Learned Priors
Chin-Yang Lin, Chung-Ho Wu, Changhan Yeh et al.
Few-Shot Recognition via Stage-Wise Retrieval-Augmented Finetuning
Tian Liu, Huixin Zhang, Shubham Parashar et al.
Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot Learning
Rashindrie Perera, Saman Halgamuge
Benchmarking Spurious Bias in Few-Shot Image Classifiers
Guangtao Zheng, Wenqian Ye, Aidong Zhang
DeIL: Direct-and-Inverse CLIP for Open-World Few-Shot Learning
Shuai Shao, Yu Bai, Yan WANG et al.
Leveraging Normalization Layer in Adapters with Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot Learning
YongJin Yang, Taehyeon Kim, Se-Young Yun
A Dynamic Learning Method towards Realistic Compositional Zero-Shot Learning
Xiaoming Hu, Zilei Wang
Adversarially Robust Few-shot Learning via Parameter Co-distillation of Similarity and Class Concept Learners
Junhao Dong, Piotr Koniusz, Junxi Chen et al.
Instance-based Max-margin for Practical Few-shot Recognition
Minghao Fu, Ke Zhu
Self-Prompt Mechanism for Few-Shot Image Recognition
Mingchen Song, Huiqiang Wang, Guoqiang Zhong
Liberating Seen Classes: Boosting Few-Shot and Zero-Shot Text Classification via Anchor
Han Liu, Siyang Zhao, Xiaotong Zhang et al.
ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models
Yassir Bendou, Amine Ouasfi, Vincent Gripon et al.
Logits DeConfusion with CLIP for Few-Shot Learning
Shuo Li, Fang Liu, Zehua Hao et al.
Few-Shot, No Problem: Descriptive Continual Relation Extraction
Nguyen Xuan Thanh, Anh Duc Le, Quyen Tran et al.
Scaling Few-Shot Learning for the Open World
Zhipeng Lin, Wenjing Yang, Haotian Wang et al.
Provably Improving Generalization of Few-shot models with Synthetic Data
Lan-Cuong Nguyen, Quan Nguyen-Tri, Bang Khanh et al.
UniFS: Universal Few-shot Instance Perception with Point Representations
Sheng Jin, Ruijie Yao, Lumin Xu et al.
TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection
Yoon Gyo Jung, Jaewoo Park, Jaeho Yoon et al.
SEC-Prompt:SEmantic Complementary Prompting for Few-Shot Class-Incremental Learning
Ye Liu, Meng Yang
Object-level Correlation for Few-Shot Segmentation
chunlin wen, Yu Zhang, Jie Fan et al.
Tripartite Weight-Space Ensemble for Few-Shot Class-Incremental Learning
Juntae Lee, Munawar Hayat, Sungrack Yun
DiffCLIP: Few-shot Language-driven Multimodal Classifier
Jiaqing Zhang, Mingxiang Cao, Xue Yang et al.
VT-FSL: Bridging Vision and Text with LLMs for Few-Shot Learning
Wenhao Li, Qiangchang Wang, Xianjing Meng et al.
Task-Specific Preconditioner for Cross-Domain Few-Shot Learning
Suhyun Kang, Jungwon Park, Wonseok Lee et al.
Unlocking the Potential of Black-box Pre-trained GNNs for Graph Few-shot Learning
Qiannan Zhang, Shichao Pei, Yuan Fang et al.
Few-Shot Domain Adaptation for Learned Image Compression
Tianyu Zhang, Haotian Zhang, Yuqi Li et al.
Causal Disentanglement and Cross-Modal Alignment for Enhanced Few-Shot Learning
Tianjiao Jiang, Zhen Zhang, Yuhang Liu et al.
Verbalized Representation Learning for Interpretable Few-Shot Generalization
Cheng-Fu Yang, Da Yin, Wenbo Hu et al.
Provoking Multi-modal Few-Shot LVLM via Exploration-Exploitation In-Context Learning
Cheng Chen, Yunpeng Zhai, Yifan Zhao et al.
Attraction Diminishing and Distributing for Few-Shot Class-Incremental Learning
Li-Jun Zhao, Zhen-Duo Chen, Yongxin Wang et al.
Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning
Yongwei Jiang, Yixiong Zou, Yuhua Li et al.
Multimodal Cross-Domain Few-Shot Learning for Egocentric Action Recognition
Masashi Hatano, Ryo Hachiuma, Ryo Fujii et al.
Is Meta-Learning Out? Rethinking Unsupervised Few-Shot Classification with Limited Entropy
Yunchuan Guan, Yu Liu, Ke Zhou et al.
On the Approximation Risk of Few-Shot Class-Incremental Learning
Xuan Wang, Zhong Ji, Xiyao Liu et al.
Adapting In-Domain Few-Shot Segmentation to New Domains without Source Domain Retraining
Qi Fan, Kaiqi Liu, Nian Liu et al.
Unleashing In-context Learning of Autoregressive Models for Few-shot Image Manipulation
Bolin Lai, Felix Juefei-Xu, Miao Liu et al.
Learning to Obstruct Few-Shot Image Classification over Restricted Classes
Amber Yijia Zheng, Chiao-An Yang, Raymond Yeh
Unknown Text Learning for CLIP-based Few-Shot Open-set Recognition
Rui Ma, Qilong Wang, Bing Cao et al.
Rejection Sampling IMLE: Designing Priors for Better Few-Shot Image Synthesis
Chirag Vashist, Shichong Peng, Ke Li
Task-Specific Gradient Adaptation for Few-Shot One-Class Classification
Yunlong Li, Xiabi Liu, Liyuan Pan et al.
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
ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning
Haoyuan Yang, Xiaoou Li, Jiaming Lv et al.
Few-Shot Pattern Detection via Template Matching and Regression
Eunchan Jo, Dahyun Kang, Sanghyun Kim et al.
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.
ArtEditor: Learning Customized Instructional Image Editor from Few-Shot Examples
Shijie Huang, Yiren Song, Yuxuan Zhang et al.
UNEM: UNrolled Generalized EM for Transductive Few-Shot Learning
Long Zhou, Fereshteh Shakeri, Aymen Sadraoui et al.
Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation
Nairouz Mrabah, Nicolas Richet, Ismail Ayed et al.
Adaptive Multi-task Learning for Few-shot Object Detection
Yan Ren, Yanling Li, Wai-Kin Adams Kong
Collaborative Consortium of Foundation Models for Open-World Few-Shot Learning
Shuai Shao, Yu Bai, Yan Wang et al.
Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling
Jie Han, Yixiong Zou, Haozhao Wang et al.
Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations
Yi Zhang, Chun-Wun Cheng, Junyi He et al.
FSL-Rectifier: Rectify Outliers in Few-Shot Learning via Test-Time Augmentation
Yunwei Bai, Ying Kiat Tan, Shiming Chen et al.
Strong Baselines for Parameter-Efficient Few-Shot Fine-Tuning
Samyadeep Basu, Shell Hu, Daniela Massiceti et al.
One-for-All Few-Shot Anomaly Detection via Instance-Induced Prompt Learning
Wenxi Lv, Qinliang Su, Wenchao Xu
UniAP: Towards Universal Animal Perception in Vision via Few-Shot Learning
Meiqi Sun, Zhonghan Zhao, Wenhao Chai et al.
Detect Any Keypoints: An Efficient Light-Weight Few-Shot Keypoint Detector
Changsheng Lu, Piotr Koniusz
Dual-Level Curriculum Meta-Learning for Noisy Few-Shot Learning Tasks
Xiaofan Que, Qi Yu
Task-Adaptive Prompted Transformer for Cross-Domain Few-Shot Learning
Jiamin Wu, Xin Liu, Xiaotian Yin et al.
Few-Shot Incremental Learning via Foreground Aggregation and Knowledge Transfer for Audio-Visual Semantic Segmentation
Jingqiao Xiu, Mengze Li, Zongxin Yang et al.
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.
Adaptive Decision Boundary for Few-Shot Class-Incremental Learning
Linhao Li, Yongzhang Tan, Siyuan Yang et al.
Less Is More: Token Context-Aware Learning for Object Tracking
Chenlong Xu, Bineng Zhong, Qihua Liang et al.
Self-Training Based Few-Shot Node Classification by Knowledge Distillation
Zongqian Wu, Yujie Mo, Peng Zhou et al.
Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts
Jiawen Zhu, Guansong Pang
One-Shot Open Affordance Learning with Foundation Models
Gen Li, Deqing Sun, Laura Sevilla-Lara et al.
Learning to Manipulate Under Limited Information
Wesley H. Holliday, Alexander Kristoffersen, Eric Pacuit
Making Large Vision Language Models to Be Good Few-Shot Learners
Fan Liu, Wenwen Cai, Jian Huo et al.
Rethinking Prior Information Generation with CLIP for Few-Shot Segmentation
Jin Wang, Bingfeng Zhang, Jian Pang et al.
Pseudo Informative Episode Construction for Few-Shot Class-Incremental Learning
Chaofan Chen, Xiaoshan Yang, Changsheng Xu
M2SD:Multiple Mixing Self-Distillation for Few-Shot Class-Incremental Learning
Jinhao Lin, Ziheng Wu, Weifeng Lin et al.
FedFSL-CFRD: Personalized Federated Few-Shot Learning with Collaborative Feature Representation Disentanglement
Shanfeng Wang, Jianzhao Li, Zaitian Liu et al.
From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation
Xingchen Wan, Han Zhou, Ruoxi Sun et al.
Graph Few-Shot Learning via Adaptive Spectrum Experts and Cross-Set Distribution Calibration
Yonghao Liu, Yajun Wang, Chunli Guo et al.
Optimization Inspired Few-Shot Adaptation for Large Language Models
Boyan Gao, Xin Wang, Yibo Yang et al.
Domain-Specific Pruning of Large Mixture-of-Experts Models with Few-shot Demonstrations
Zican Dong, Han Peng, Peiyu Liu et al.
Few-Shot Knowledge Distillation of LLMs With Counterfactual Explanations
Faisal Hamman, Pasan Dissanayake, Yanjun Fu et al.
Optimal Transport of Diverse Unsupervised Tasks for Robust Learning from Noisy Few-Shot Data
Xiaofan Que, Qi Yu
MoEMeta: Mixture-of-Experts Meta Learning for Few-Shot Relational Learning
Han Wu, Jie Yin
Federated Few-Shot Class-Incremental Learning
Muhammad Anwar Masum, Mahardhika Pratama, Lin Liu et al.
In-Context Principle Learning from Mistakes
Tianjun Zhang, Aman Madaan, Luyu Gao et al.
Few-shot Adaptation to Distribution Shifts By Mixing Source and Target Embeddings
Yihao Xue, Ali Payani, Yu Yang et al.
Compositional Few-Shot Class-Incremental Learning
Yixiong Zou, Shanghang Zhang, haichen zhou et al.
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Xinyu Tang, Richard Shin, Huseyin Inan et al.