Few-Shot Learning
Learning from very few examples
Top Papers
Fast Machine Unlearning without Retraining through Selective Synaptic Dampening
Jack Foster, Stefan Schoepf, Alexandra Brintrup
FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering
Zhenyu Li, Sunqi Fan, Yu Gu et al.
Making Text Embedders Few-Shot Learners
Chaofan Li, Minghao Qin, Shitao Xiao et al.
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-Rodríguez, Sina Hajimiri, Ismail Ben Ayed et al.
Few-Shot Object Detection with Foundation Models
Guangxing Han, Ser-Nam Lim
Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector
Yuqian Fu, Yu Wang, Yixuan Pan et al.
How Two-Layer Neural Networks Learn, One (Giant) Step at a Time
Yatin Dandi, Florent Krzakala, Bruno Loureiro et al.
Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners
Keon Hee Park, Kyungwoo Song, Gyeong-Moon Park
Knowledge-Enhanced Dual-stream Zero-shot Composed Image Retrieval
Yucheng Suo, Fan Ma, Linchao Zhu et al.
Cross-Layer and Cross-Sample Feature Optimization Network for Few-Shot Fine-Grained Image Classification
Zhen-Xiang Ma, Zhen-Duo Chen, Li-Jun Zhao et al.
Fine-Grained Prototypes Distillation for Few-Shot Object Detection
Zichen Wang, Bo Yang, Haonan Yue et al.
DAVE - A Detect-and-Verify Paradigm for Low-Shot Counting
Jer Pelhan, Alan Lukezic, Vitjan Zavrtanik et al.
Does CLIP’s generalization performance mainly stem from high train-test similarity?
Prasanna Mayilvahanan, Thaddäus Wiedemer, Evgenia Rusak 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 Akyürek, 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.
Relevant Intrinsic Feature Enhancement Network for Few-Shot Semantic Segmentation
Xiaoyi Bao, Jie Qin, Siyang Sun et al.
CAT-SAM: Conditional Tuning for Few-Shot Adaptation of Segment Anything Model
Aoran Xiao, Weihao Xuan, Heli Qi et al.
Machine Unlearning Fails to Remove Data Poisoning Attacks
Martin Pawelczyk, Jimmy Di, Yiwei Lu et al.
No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation
Xiangyang Zhu, Renrui Zhang, Bowei He et al.
TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning
Andreas Auer, Patrick Podest, Daniel Klotz et al.
Adapt Before Comparison: A New Perspective on Cross-Domain Few-Shot Segmentation
Jonas Herzog
Exploring Unbiased Deepfake Detection via Token-Level Shuffling and Mixing
Xinghe Fu, Zhiyuan Yan, Taiping Yao et al.
AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learning
Yuwei Tang, ZhenYi Lin, Qilong Wang et al.
Does Few-Shot Learning Suffer from Backdoor Attacks?
Xinwei Liu, Xiaojun Jia, Jindong Gu et al.
Summarizing Stream Data for Memory-Constrained Online Continual Learning
Jianyang Gu, Kai Wang, Wei Jiang et al.
Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning
Somnath Basu Roy Chowdhury, Krzysztof Choromanski, Arijit Sehanobish et al.
Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning
Yixiong Zou, Yicong Liu, Yiman Hu et al.
Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detection
Zhen Qu, Xian Tao, Xinyi Gong et al.
Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype Enhancement
Jing Wang, Jiangyun Li, Chen Chen et al.
ElasticTok: Adaptive Tokenization for Image and Video
Wilson Yan, Volodymyr Mnih, Aleksandra Faust et al.
Large Language Models are Good Prompt Learners for Low-Shot Image Classification
Zhaoheng Zheng, Jingmin Wei, Xuefeng Hu et al.
Few-Shot Anomaly-Driven Generation for Anomaly Classification and Segmentation
Guan Gui, Bin-Bin Gao, Jun Liu et al.
MetaCoCo: A New Few-Shot Classification Benchmark with Spurious Correlation
Min Zhang, Haoxuan Li, Fei Wu et al.
CLOSER: Towards Better Representation Learning for Few-Shot Class-Incremental Learning
Junghun Oh, Sungyong Baik, Kyoung Mu Lee
Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detection
Fenfang Tao, Guo-Sen Xie, Fang Zhao et al.
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.
Event Camera Data Dense Pre-training
Yan Yang, Liyuan Pan, Liu liu
Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction
Vaishnavh Nagarajan, Chen Wu, Charles Ding et al.
Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot Learning
Rashindrie Perera, Saman Halgamuge
Lipsum-FT: Robust Fine-Tuning of Zero-Shot Models Using Random Text Guidance
Giung Nam, Byeongho Heo, Juho Lee
SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuning
Zhewei Dai, Shilei Zeng, Haotian Liu et al.
Task-Disruptive Background Suppression for Few-Shot Segmentation
Suho Park, SuBeen Lee, Sangeek Hyun et al.
DeIL: Direct-and-Inverse CLIP for Open-World Few-Shot Learning
Shuai Shao, Yu Bai, Yan WANG et al.
Synergistic Anchored Contrastive Pre-training for Few-Shot Relation Extraction
Da Luo, Yanglei Gan, Rui Hou et al.
Benchmarking Spurious Bias in Few-Shot Image Classifiers
Guangtao Zheng, Wenqian Ye, Aidong Zhang
Understanding prompt engineering may not require rethinking generalization
Victor Akinwande, Yiding Jiang, Dylan Sam et al.
Epitopological learning and Cannistraci-Hebb network shape intelligence brain-inspired theory for ultra-sparse advantage in deep learning
Yingtao Zhang, Jialin Zhao, Wenjing Wu et al.
DeepCalliFont: Few-Shot Chinese Calligraphy Font Synthesis by Integrating Dual-Modality Generative Models
Yitian Liu, Zhouhui Lian
ZOOM: Learning Video Mirror Detection with Extremely-Weak Supervision
Ke Xu, Tsun Wai Siu, Rynson W.H. Lau
SMILe: Leveraging Submodular Mutual Information For Robust Few-Shot Object Detection
Anay Majee, Ryan X Sharp, Rishabh Iyer
MVREC: A General Few-shot Defect Classification Model Using Multi-View Region-Context
Shuai Lyu, Rongchen Zhang, Zeqi Ma et al.
Adversarially Robust Few-shot Learning via Parameter Co-distillation of Similarity and Class Concept Learners
Junhao Dong, Piotr Koniusz, Junxi Chen 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
Synthetic Prior for Few-Shot Drivable Head Avatar Inversion
Wojciech Zielonka, Stephan J. Garbin, Alexandros Lattas et al.
A Dynamic Learning Method towards Realistic Compositional Zero-Shot Learning
Xiaoming Hu, Zilei Wang
Capture Global Feature Statistics for One-Shot Federated Learning
Zenghao Guan, Yucan Zhou, Xiaoyan Gu
Instance-based Max-margin for Practical Few-shot Recognition
Minghao Fu, Ke Zhu
TTT-MIM: Test-Time Training with Masked Image Modeling for Denoising Distribution Shifts
Youssef Mansour, Xuyang Zhong, Serdar Caglar et al.
FALIP: Visual Prompt as Foveal Attention Boosts CLIP Zero-Shot Performance
Jiedong Zhuang, Jiaqi Hu, Lianrui Mu et al.
Catch-Up Mix: Catch-Up Class for Struggling Filters in CNN
Minsoo Kang, Minkoo Kang, Suhyun Kim
FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification
Zhengrui Guo, Conghao Xiong, Jiabo MA et al.
H-ensemble: An Information Theoretic Approach to Reliable Few-Shot Multi-Source-Free Transfer
Yanru Wu, Jianning Wang, Weida Wang et al.
Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models
Lei Tang, Jinghui Qin, Wenxuan Ye 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.
Liberating Seen Classes: Boosting Few-Shot and Zero-Shot Text Classification via Anchor
Han Liu, Siyang Zhao, Xiaotong Zhang et al.
Self-Prompt Mechanism for Few-Shot Image Recognition
Mingchen Song, Huiqiang Wang, Guoqiang Zhong
Few-Shot, No Problem: Descriptive Continual Relation Extraction
Nguyen Xuan Thanh, Anh Duc Le, Quyen Tran et al.
Learning with Unreliability: Fast Few-shot Voxel Radiance Fields with Relative Geometric Consistency
Xu Yingjie, Bangzhen Liu, Hao Tang et al.
Demystifying Language Model Forgetting with Low-rank Example Associations
Xisen Jin, Xiang Ren
Minimalist Vision with Freeform Pixels
Jeremy Klotz, Shree Nayar
OpenKD: Opening Prompt Diversity for Zero- and Few-shot Keypoint Detection
Changsheng Lu, Zheyuan Liu, Piotr Koniusz
Building Variable-Sized Models via Learngene Pool
Boyu Shi, Shiyu Xia, Xu Yang et al.
Scaling Few-Shot Learning for the Open World
Zhipeng Lin, Wenjing Yang, Haotian Wang et al.
How Far Are We from True Unlearnability?
Kai Ye, Liangcai Su, Chenxiong Qian
One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models
Sheng-Jun Huang, Yi Li, Yiming Sun et al.
Multiplane Prior Guided Few-Shot Aerial Scene Rendering
Zihan Gao, Licheng Jiao, Lingling Li et al.
Reconstruction Target Matters in Masked Image Modeling for Cross-Domain Few-Shot Learning
Ran Ma, Yixiong Zou, Yuhua Li et al.
UniFS: Universal Few-shot Instance Perception with Point Representations
Sheng Jin, Ruijie Yao, Lumin Xu et al.
VQToken: Neural Discrete Token Representation Learning for Extreme Token Reduction in Video Large Language Models
Haichao Zhang, Yun Fu
Predicting the Susceptibility of Examples to Catastrophic Forgetting
Guy Hacohen, Tinne Tuytelaars
Self-Ensembling Gaussian Splatting for Few-Shot Novel View Synthesis
Chen Zhao, Xuan Wang, Tong Zhang et al.
A3: Few-shot Prompt Learning of Unlearnable Examples with Cross-Modal Adversarial Feature Alignment
Xuan Wang, Xitong Gao, Dongping Liao et al.
Provably Improving Generalization of Few-shot models with Synthetic Data
Lan-Cuong Nguyen, Quan Nguyen-Tri, Bang Khanh et al.
TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection
Yoon Gyo Jung, Jaewoo Park, Jaeho Yoon et al.
DiffCLIP: Few-shot Language-driven Multimodal Classifier
Jiaqing Zhang, Mingxiang Cao, Xue Yang et al.
Prototype antithesis for biological few-shot class-incremental learning
Binghao Liu, Han Yang, Fang Wan et al.
AmorLIP: Efficient Language-Image Pretraining via Amortization
Haotian Sun, Yitong Li, Yuchen Zhuang et al.
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
VT-FSL: Bridging Vision and Text with LLMs for Few-Shot Learning
Wenhao Li, Qiangchang Wang, Xianjing Meng et al.
Learnable Feature Patches and Vectors for Boosting Low-light Image Enhancement without External Knowledge
Xiaogang Xu, Jiafei Wu, Qingsen Yan et al.
Do ImageNet-trained Models Learn Shortcuts? The Impact of Frequency Shortcuts on Generalization
Shunxin Wang, Raymond Veldhuis, Nicola Strisciuglio
Few-shot Personalized Scanpath Prediction
Ruoyu Xue, Jingyi Xu, Sounak Mondal et al.