A list of awesome few-shot learning resources, inspired by AWESOME.
Entity Format in Markdown:
[n] **Paper Name.**
Author 1, Author 2, ..., Author n.
In conference/journal, year.
[[paper](url)]
[[code](url)]
[1] Adversarially Robust Few-shot Learning via Parameter Co-distillation of Similarity and Class Concept Learners. Junhao Dong, Piotr Koniusz, Junxi Chen, Xiaohua Xie, Yew-Soon Ong. In CVPR, 2024. [paper] [code]
[2] Simple Semantic-Aided Few-Shot Learning. Hai Zhang, Junzhe Xu, Shanlin Jiang, Zhenan He. In CVPR, 2024. [paper] [code]
[3] Frozen Feature Augmentation for Few-Shot Image Classification. Andreas Bär, Neil Houlsby, Mostafa Dehghani, Manoj Kumar. In CVPR, 2024. [paper] [code]
[4] Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning. Yixiong Zou, Yicong Liu, Yiman Hu, Yuhua Li, Ruixuan Li. In CVPR, 2024. [paper] [code]
[5] AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learning. Yuwei Tang, Zhenyi Lin, Qilong Wang, Pengfei Zhu, Qinghua Hu. In CVPR, 2024. [paper] [code]
[6] Towards Generalizing to Unseen Domains with Few Labels. Chamuditha Jayanga Galappaththige, Sanoojan Baliah, Malitha Gunawardhana, Muhammad Haris Khan. In CVPR, 2024. [paper] [code]
[7] Instance-based Max-margin for Practical Few-shot Recognition. Minghao Fu, Ke Zhu, Jianxin Wu. In CVPR, 2024. [paper] [code]
[8] OrCo: Towards Better Generalization via Orthogonality and Contrast for Few-Shot Class-Incremental Learning. Noor Ahmed, Anna Kukleva, Bernt Schiele. In CVPR, 2024. [paper] [code]
[9] Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot Learning. Rashindrie Perera, Saman Halgamuge. In CVPR, 2024. [paper] [code]
[10] Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners. Keon-Hee Park, Kyungwoo Song, Gyeong-Moon Park. In CVPR, 2024. [paper] [code]
[11] DeIL: Direct-and-Inverse CLIP for Open-World Few-Shot Learning. Shuai Shao, Yu Bai, Yan Wang, Baodi Liu, Yicong Zhou. In CVPR, 2024. [paper] [code]
[12] A Closer Look at the Few-Shot Adaptation of Large Vision-Language Models. Julio Silva-Rodríguez, Sina Hajimiri, Ismail Ben Ayed, Jose Dolz. In CVPR, 2024. [paper] [code]
[13] Neural Fine-Tuning Search for Few-Shot Learning. Panagiotis Eustratiadis, Łukasz Dudziak, Da Li, Timothy Hospedales. In ICLR, 2024. [paper] [code]
[14] A Hierarchical Bayesian Model for Deep Few-Shot Meta Learning. Minyoung Kim, Timothy Hospedales. In ICLR, 2024. [paper] [code]
[15] BECLR: Batch Enhanced Contrastive Few-Shot Learning. Stylianos Poulakakis-Daktylidis, Hadi Jamali-Rad. In ICLR, 2024. [paper] [code]
[16] MetaCoCo: A New Few-Shot Classification Benchmark with Spurious Correlation. Min Zhang, Haoxuan Li, Fei Wu, Kun Kuang. In ICLR, 2024. [paper] [code]
[17] Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning. Zhuoyan Xu, Zhenmei Shi, Junyi Wei, Fangzhou Mu, Yin Li, Yingyu Liang. In ICLR, 2024. [paper] [code]
[18] Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation. Yuan Yuan, Chenyang Shao, Jingtao Ding, Depeng Jin, Yong Li. In ICLR, 2024. [paper] [code]
[1] Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners. Renrui Zhang, Xiangfei Hu, Bohao Li, Siyuan Huang, Hanqiu Deng, Hongsheng Li, Yu Qiao, Peng Gao. In CVPR, 2023. [paper] [code]
[2] Revisiting Prototypical Network for Cross Domain Few-Shot Learning. Fei Zhou, Peng Wang, Lei Zhang, Wei Wei, Yanning Zhang. In CVPR, 2023. [paper] [code]
[3] Glocal Energy-based Learning for Few-Shot Open-Set Recognition. Haoyu Wang, Guansong Pang, Peng Wang, Lei Zhang, Wei Wei, Yanning Zhang. In CVPR, 2023. [paper] [code]
[4] Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models. Zhiqiu Lin, Samuel Yu, Zhiyi Kuang, Deepak Pathak, Deva Ramanan. In CVPR, 2023. [paper] [code]
[5] Semantic Prompt for Few-Shot Image Recognition. Wentao Chen, Chenyang Si, Zhang Zhang, Liang Wang, Zilei Wang, Tieniu Tan. In CVPR, 2023. [paper] [code]
[6] Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinement. Hao Zhu, Piotr Koniusz. In CVPR, 2023. [paper] [code]
[7] GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-Shot Class Incremental Task. Huiping Zhuang, Zhenyu Weng, Run He, Zhiping Lin, Ziqian Zeng. In CVPR, 2023. [paper] [code]
[8] Few-Shot Class-Incremental Learning via Class-Aware Bilateral Distillation. Linglan Zhao, Jing Lu, Yunlu Xu, Zhanzhan Cheng, Dashan Guo, Yi Niu, Xiangzhong Fang. In CVPR, 2023. [paper] [code]
[9] Open-Set Likelihood Maximization for Few-Shot Learning. Malik Boudiaf, Etienne Bennequin, Myriam Tami, Antoine Toubhans, Pablo Piantanida, Céline Hudelot, Ismail Ben Ayed. In CVPR, 2023. [paper] [code]
[10] Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation. Dahyun Kang, Piotr Koniusz, Minsu Cho, Naila Murray. In CVPR, 2023. [paper] [code]
[11] Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings. Daniel J. Trosten, Rwiddhi Chakraborty, Sigurd Løkse, Kristoffer Knutsen Wickstrøm, Robert Jenssen, Michael C. Kampffmeyer. In CVPR, 2023. [paper] [code]
[12] Supervised Masked Knowledge Distillation for Few-Shot Transformers. Han Lin, Guangxing Han, Jiawei Ma, Shiyuan Huang, Xudong Lin, Shih-Fu Chang. In CVPR, 2023. [paper] [code]
[13] Bi-Level Meta-Learning for Few-Shot Domain Generalization. Xiaorong Qin, Xinhang Song, Shuqiang Jiang. In CVPR, 2023. [paper] [code]
[14] ProD: Prompting-To-Disentangle Domain Knowledge for Cross-Domain Few-Shot Image Classification. Tianyi Ma, Yifan Sun, Zongxin Yang, Yi Yang. In CVPR, 2023. [paper] [code]
[15] Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning. Zeyin Song, Yifan Zhao, Yujun Shi, Peixi Peng, Li Yuan, Yonghong Tian. In CVPR, 2023. [paper] [code]
[16] Boosting Transductive Few-Shot Fine-Tuning With Margin-Based Uncertainty Weighting and Probability Regularization. Ran Tao, Hao Chen, Marios Savvides. In CVPR, 2023. [paper] [code]
[17] StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning. Yuqian Fu, Yu Xie, Yanwei Fu, Yu-Gang Jiang. In CVPR, 2023. [paper] [code]
[18] WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation. Jongheon Jeong, Yang Zou, Taewan Kim, Dongqing Zhang, Avinash Ravichandran, Onkar Dabeer. In CVPR, 2023. [paper] [code]
[19] Few-Shot Learning with Visual Distribution Calibration and Cross-Modal Distribution Alignment. Runqi Wang, Hao Zheng, Xiaoyue Duan, Jianzhuang Liu, Yuning Lu, Tian Wang, Songcen Xu, Baochang Zhang. In CVPR, 2023. [paper] [code]
[20] Domain Adaptive Few-Shot Open-Set Learning. Debabrata Pal, Deeptej More, Sai Bhargav, Dipesh Tamboli, Vaneet Aggarwal, Biplab Banerjee. In ICCV, 2023. [paper] [code]
[21] StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation. Aibek Alanov, Vadim Titov, Maksim Nakhodnov, Dmitry Vetrov. In ICCV, 2023. [paper] [code]
[22] Prototypes-oriented Transductive Few-shot Learning with Conditional Transport. Long Tian, Jingyi Feng, Wenchao Chen, Xiaoqiang Chai, Liming Wang, Xiyang Liu, Bo Chen. In ICCV, 2023. [paper] [code]
[23] Few-shot Continual Infomax Learning. Ziqi Gu, Chunyan Xu, Jian Yang, Zhen Cui. In ICCV, 2023. [paper] [code]
[24] Task-aware Adaptive Learning for Cross-domain Few-shot Learning. Yurong Guo, Ruoyi Du, Yuan Dong, Timothy Hospedales, Yi-Zhe Song, Zhanyu Ma. In ICCV, 2023. [paper] [code]
[25] DETA: Denoised Task Adaptation for Few-Shot Learning. Ji Zhang, Lianli Gao, Xu Luo, Hengtao Shen, Jingkuan Song. In ICCV, 2023. [paper] [code]
[26] Class-Aware Patch Embedding Adaptation for Few-Shot Image Classification. Fusheng Hao, Fengxiang He, Liu Liu, Fuxiang Wu, Dacheng Tao, Jun Cheng. In ICCV, 2023. [paper] [code]
[27] Frequency Guidance Matters in Few-Shot Learning. Hao Cheng, Siyuan Yang, Joey Tianyi Zhou, Lanqing Guo, Bihan Wen. In ICCV, 2023. [paper] [code]
[28] Unsupervised Meta-learning via Few-shot Pseudo-supervised Contrastive Learning. Huiwon Jang, Hankook Lee, Jinwoo Shin. In ICLR, 2023. [paper] [code]
[29] Warping the Space: Weight Space Rotation for Class-Incremental Few-Shot Learning. Do-Yeon Kim, Dong-Jun Han, Jun Seo, Jaekyun Moon. In ICLR, 2023. [paper] [code]
[30] Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class Incremental Learning. Yibo Yang, Haobo Yuan, Xiangtai Li, Zhouchen Lin, Philip Torr, Dacheng Tao. In ICLR, 2023. [paper] [code]
[31] Progressive Mix-Up for Few-Shot Supervised Multi-Source Domain Transfer. Ronghang Zhu, Ronghang Zhu, Xiang Yu, Sheng Li. In ICLR, 2023. [paper] [code]
[32] Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning. Ivona Najdenkoska, Xiantong Zhen, Marcel Worring. In ICLR, 2023. [paper] [code]
[33] Contrastive Meta-Learning for Partially Observable Few-Shot Learning. Adam Jelley, Amos Storkey, Antreas Antoniou, Sam Devlin. In ICLR, 2023. [paper] [code]
[34] Revisit Finetuning strategy for Few-Shot Learning to Strengthen the Equivariance of Emdeddings. Heng Wang, Tan Yue, Xiang Ye, Zihang He, Bohan Li, Yong Li. In ICLR, 2023. [paper] [code]
[35] On the Soft-Subnetwork for Few-shot Class Incremental Learning. Haeyong Kang, Jaehong Yoon, Sultan Rizky Hikmawan Madjid, Sung Ju Hwang, Chang D. Yoo. In ICLR, 2023. [paper] [code]
[36] Hard-Meta-Dataset++: Towards Understanding Few-Shot Performance on Difficult Tasks. Samyadeep Basu, Megan Stanley, John F Bronskill, Soheil Feizi, Daniela Massiceti. In ICLR, 2023. [paper] [code]
[37] Context-enriched molecule representations improve few-shot drug discovery. Johannes Schimunek, Philipp Seidl, Lukas Friedrich, Daniel Kuhn, Friedrich Rippmann, Sepp Hochreiter, Günter Klambauer. In ICLR, 2023. [paper] [code]
[38] FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification. Aliaksandra Shysheya, John Bronskill, Massimiliano Patacchiola, Sebastian Nowozin, Richard E Turner. In ICLR, 2023. [paper] [code]
[39] Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot Classification. Hao ZHENG, Runqi Wang, Jianzhuang Liu, Asako Kanezaki. In ICLR, 2023. [paper] [code]
[40] Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration. Qi-Wei Wang, Da-Wei Zhou, Yi-Kai Zhang, De-Chuan Zhan, Han-Jia Ye. In NeurIPS, 2023. [paper] [code]
[41] Understanding Few-Shot Learning: Measuring Task Relatedness and Adaptation Difficulty via Attributes. Minyang Hu, Hong Chang, Zong Guo, Bingpeng MA, Shiguang Shan, Xilin Chen. In NeurIPS, 2023. [paper] [code]
[42] Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification. Tianjun Ke, Haoqun Cao, Zenan Ling, Feng Zhou. In NeurIPS, 2023. [paper] [code]
[43] FD-Align: Feature Discrimination Alignment for Fine-tuning Pre-Trained Models in Few-Shot Learning. Kun Song, Huimin Ma, Bochao Zou, Huishuai Zhang, Weiran Huang. In NeurIPS, 2023. [paper] [code]
[44] DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank Correlation. Kaipeng Zheng, Huishuai Zhang, Weiran Huang. In NeurIPS, 2023. [paper] [code]
[45] Meta-Adapter: An Online Few-shot Learner for Vision-Language Model. Cheng Cheng, Lin Song, Ruoyi Xue, Hang Wang, Hongbin Sun, Yixiao Ge, Ying Shan. In NeurIPS, 2023. [paper] [code]
[46] Focus Your Attention when Few-Shot Classification. Haoqing Wang, Shibo Jie, Zhihong Deng. In NeurIPS, 2023. [paper] [code]
[47] Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification. Neel Guha, Mayee F. Chen, Kush Bhatia, Azalia Mirhoseini, Frederic Sala, Christopher Ré. In NeurIPS, 2023. [paper] [code]
[48] Meta-AdaM: An Meta-Learned Adaptive Optimizer with Momentum for Few-Shot Learning. Siyuan Sun, Hongyang Gao. In NeurIPS, 2023. [paper] [code]
[49] Alignment with human representations supports robust few-shot learning. Ilia Sucholutsky, Thomas L. Griffiths. In NeurIPS, 2023. [paper] [code]
[1] Learning to Affiliate: Mutual Centralized Learning for Few-shot Classification. Yang Liu, Weifeng Zhang, Chao Xiang, Tu Zheng, Deng Cai, Xiaofei He. In CVPR, 2022. [paper] [code]
[2] Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning. Moslem Yazdanpanah, Aamer Abdul Rahman, Muawiz Chaudhary, Christian Desrosiers, Mohammad Havaei, Eugene Belilovsky, Samira Ebrahimi Kahou. In CVPR, 2022. [paper] [code]
[3] Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference. Shell Xu Hu, Da Li, Jan Stühmer, Minyoung Kim, Timothy M. Hospedales. In CVPR, 2022. [paper] [code]
[4] Uni-Perceiver: Pre-training Unified Architecture for Generic Perception for Zero-shot and Few-shot Tasks. Xizhou Zhu, Jinguo Zhu, Hao Li, Xiaoshi Wu, Xiaogang Wang, Hongsheng Li, Xiaohua Wang, Jifeng Dai. In CVPR, 2022. [paper] [code]
[5] Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning. Yangji He, Weihan Liang, Dongyang Zhao, Hong-Yu Zhou, Weifeng Ge, Yizhou Yu, Wenqiang Zhang. In CVPR, 2022. [paper] [code]
[6] Task-Adaptive Negative Envision for Few-Shot Open-Set Recognition. Shiyuan Huang, Jiawei Ma, Guangxing Han, Shih-Fu Chang. In CVPR, 2022. [paper] [code]
[7] CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification. Philip Chikontwe, Soopil Kim, Sang Hyun Park. In CVPR, 2022. [paper] [code]
[8] Forward Compatible Few-Shot Class-Incremental Learning. Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma, Shiliang Pu, De-Chuan Zhan. In CVPR, 2022. [paper] [code]
[9] Ranking Distance Calibration for Cross-Domain Few-Shot Learning. Pan Li, Shaogang Gong, Chengjie Wang, Yanwei Fu. In CVPR, 2022. [paper] [code]
[10] Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations. Junhao Dong, Yuan Wang, Jian-Huang Lai, Xiaohua Xie. In CVPR, 2022. [paper] [code]
[11] Generating Representative Samples for Few-Shot Classification. Jingyi Xu, Hieu Le. In CVPR, 2022. [paper] [code]
[12] Task Discrepancy Maximization for Fine-grained Few-Shot Classification. SuBeen Lee, WonJun Moon, Jae-Pil Heo. In CVPR, 2022. [paper] [code]
[13] EASE: Unsupervised Discriminant Subspace Learning for Transductive Few-Shot Learning. Hao Zhu, Piotr Koniusz. In CVPR, 2022. [paper] [code]
[14] Integrative Few-Shot Learning for Classification and Segmentation. Dahyun Kang, Minsu Cho. In CVPR, 2022. [paper] [code]
[15] Constrained Few-shot Class-incremental Learning. Michael Hersche, Geethan Karunaratne, Giovanni Cherubini, Luca Benini, Abu Sebastian, Abbas Rahimi. In CVPR, 2022. [paper] [code]
[16] MetaFSCIL: A Meta-Learning Approach for Few-Shot Class Incremental Learning. Zhixiang Chi, Li Gu, Huan Liu, Yang Wang, Yuanhao Yu, Jin Tang. In CVPR, 2022. [paper] [code]
[17] Semi-Supervised Few-Shot Learning via Multi-Factor Clustering. Jie Ling, Lei Liao, Meng Yang, Jia Shuai. In CVPR, 2022. [paper] [code]
[18] Cross-domain Few-shot Learning with Task-specific Adapters. Wei-Hong Li, Xialei Liu, Hakan Bilen. In CVPR, 2022. [paper] [code]
[19] Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning. Haoxiang Wang, Yite Wang, Ruoyu Sun, Bo Li. In CVPR, 2022. [paper] [code]
[20] Few-shot Learning with Noisy Labels. Kevin J Liang, Samrudhdhi B. Rangrej, Vladan Petrovic, Tal Hassner. In CVPR, 2022. [paper] [code]
[21] Few-Shot Incremental Learning for Label-to-Image Translation. Pei Chen, Yangkang Zhang, Zejian Li, Lingyun Sun. In CVPR, 2022. [paper] [code]
[22] Matching Feature Sets for Few-Shot Image Classification. Arman Afrasiyabi, Hugo Larochelle, Jean-François Lalonde, Christian Gagné. In CVPR, 2022. [paper] [code]
[23] Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification. Jiangtao Xie, Fei Long, Jiaming Lv, Qilong Wang, Peihua Li. In CVPR, 2022. [paper] [code]
[24] Meta-Learning with Fewer Tasks through Task Interpolation. Huaxiu Yao, Linjun Zhang, Chelsea Finn. In ICLR, 2022. [paper] [code]
[25] On the Importance of Firth Bias Reduction in Few-Shot Classification. Saba Ghaffari, Ehsan Saleh, David Forsyth, Yu-xiong Wang. In ICLR, 2022. [paper] [code]
[26] ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning. Debasmit Das, Sungrack Yun, Fatih Porikli. In ICLR, 2022. [paper] [code]
[27] Hierarchical Variational Memory for Few-shot Learning Across Domains. Yingjun Du, Xiantong Zhen, Ling Shao, Cees G. M. Snoek. In ICLR, 2022. [paper] [code]
[28] Subspace Regularizers for Few-Shot Class Incremental Learning. Afra Feyza Akyürek, Ekin Akyürek, Derry Tanti Wijaya, Jacob Andreas. In ICLR, 2022. [paper] [code]
[29] How to Train Your MAML to Excel in Few-Shot Classification. Han-Jia Ye, Wei-Lun Chao. In ICLR, 2022. [paper] [code]
[30] Task Affinity with Maximum Bipartite Matching in Few-Shot Learning. Cat P. Le, Juncheng Dong, Mohammadreza Soltani, Vahid Tarokh. In ICLR, 2022. [paper] [code]
[31] Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification. Bing Su, Ji-Rong Wen. In ICLR, 2022. [paper] [code]
[32] Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners. Ningyu Zhang, Luoqiu Li, Xiang Chen, Shumin Deng, Zhen Bi, Chuanqi Tan, Fei Huang, Huajun Chen. In ICLR, 2022. [paper] [code]
[33] Few-shot Learning via Dirichlet Tessellation Ensemble. Chunwei Ma, Ziyun Huang, Mingchen Gao, Jinhui Xu. In ICLR, 2022. [paper] [code]
[34] Switch to Generalize: Domain-Switch Learning for Cross-Domain Few-Shot Classification. Zhengdong Hu, Yifan Sun, Yi Yang. In ICLR, 2022. [paper] [code]
[35] Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification. Renrui Zhang, Zhang Wei, Rongyao Fang, Peng Gao, Kunchang Li, Jifeng Dai, Yu Qiao, Hongsheng Li. In ECCV, 2022. [paper] [code]
[36] Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay. Huan Liu, Li Gu, Zhixiang Chi, Yang Wang, Yuanhao Yu, Jun Chen, Jin Tang. In ECCV, 2022. [paper] [code]
[37] Self-Supervision Can Be a Good Few-Shot Learner. Yuning Lu, Liangjian Wen, Jianzhuang Liu, Yajing Liu, Xinmei Tian. In ECCV, 2022. [paper] [code]
[38] tSF: Transformer-based Semantic Filter for Few-Shot Learning. Jinxiang Lai, Siqian Yang, Wenlong Liu, Yi Zeng, Zhongyi Huang, Wenlong Wu, Jun Liu, Bin-Bin Gao, Chengjie Wang. In ECCV, 2022. [paper] [code]
[39] Adversarial Feature Augmentation for Cross-domain Few-shot Classification. Yanxu Hu, Andy J. Ma. In ECCV, 2022. [paper] [code]
[40] Worst Case Matters for Few-Shot Recognition. Minghao Fu, Yun-Hao Cao, Jianxin Wu. In ECCV, 2022. [paper] [code]
[41] DNA: Improving Few-Shot Transfer Learning with Low-Rank Decomposition and Alignment. Ziyu Jiang, Tianlong Chen, Xuxi Chen, Yu Cheng, Luowei Zhou, Lu Yuan, Ahmed Awadallah, Zhangyang Wang. In ECCV, 2022. [paper] [code]
[42] Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learning. Rongkai Ma, Pengfei Fang, Gil Avraham, Yan Zuo, Tianyu Zhu, Tom Drummond, Mehrtash Harandi. In ECCV, 2022. [paper] [code]
[43] Few-Shot Classification with Contrastive Learning. Zhanyuan Yang, Jinghua Wang, Yingying Zhu. In ECCV, 2022. [paper] [code]
[44] Coarse-To-Fine Incremental Few-Shot Learning. Xiang Xiang, Yuwen Tan, Qian Wan, Jing Ma. In ECCV, 2022. [paper] [code]
[45] Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations. Wentao Chen, Zhang Zhang, Wei Wang, Liang Wang, Zilei Wang, Tieniu Tan. In ECCV, 2022. [paper] [code]
[46] Improving Few-Shot Learning through Multi-task Representation Learning Theory. Quentin Bouniot, Ievgen Redko, Romaric Audigier, Angélique Loesch, Amaury Habrard. In ECCV, 2022. [paper] [code]
[47] Few-Shot Class-Incremental Learning from an Open-Set Perspective. Can Peng, Kun Zhao, Tianren Wang, Meng Li, Brian C. Lovell. In ECCV, 2022. [paper] [code]
[48] Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation. Min Zhang, Siteng Huang, Wenbin Li, Donglin Wang. In ECCV, 2022. [paper] [code]
[49] S3C: Self-Supervised Stochastic Classifiers for Few-Shot Class-Incremental Learning. Jayateja Kalla, Soma Biswas. In ECCV, 2022. [paper] [code]
[50] Unsupervised Few-Shot Image Classification by Learning Features into Clustering Space. Shuo Li, Fang Liu, Zehua Hao, Kaibo Zhao, Licheng Jiao. In ECCV, 2022. [paper] [code]
[51] TransVLAD: Focusing on Locally Aggregated Descriptors for Few-Shot Learning. Haoquan Li, Laoming Zhang, Daoan Zhang, Lang Fu, Peng Yang, Jianguo Zhang. In ECCV, 2022. [paper] [code]
[52] Kernel Relative-prototype Spectral Filtering for Few-shot Learning. Tao Zhang, Wu Huang. In ECCV, 2022. [paper] [code]
[53] Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inference. Ségolène Tiffany Martin, Malik Boudiaf, Emilie Chouzenoux, Jean-Christophe Pesquet, Ismail Ben Ayed. In NeurIPS, 2022. [paper] [code]
[54] Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales. Tao Liu, P. R. Kumar, Ruida Zhou, Xi Liu. In NeurIPS, 2022. [paper] [code]
[55] Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks. Daiki Chijiwa, Shin'ya Yamaguchi, Atsutoshi Kumagai, Yasutoshi Ida. In NeurIPS, 2022. [paper] [code]
[56] FeLMi : Few shot Learning with hard Mixup. Aniket Roy, Anshul Shah, Ketul Shah, Prithviraj Dhar, Anoop Cherian, Rama Chellappa. In NeurIPS, 2022. [paper] [code]
[57] Smoothed Embeddings for Certified Few-Shot Learning. Mikhail Pautov, Olesya Kuznetsova, Nurislam Tursynbek, Aleksandr Petiushko, Ivan Oseledets. In NeurIPS, 2022. [paper] [code]
[58] Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty. Jaehoon Oh, Sungnyun Kim, Namgyu Ho, Jin-Hwa Kim, Hwanjun Song, Se-Young Yun. In NeurIPS, 2022. [paper] [code]
[59] Few-shot Relational Reasoning via Connection Subgraph Pretraining. Qian Huang, Hongyu Ren, Jure Leskovec. In NeurIPS, 2022. [paper] [code]
[60] Graph Few-shot Learning with Task-specific Structures. Song Wang, Chen Chen, Jundong Li. In NeurIPS, 2022. [paper] [code]
[61] Few-shot Learning for Feature Selection with Hilbert-Schmidt Independence Criterion. Atsutoshi Kumagai, Tomoharu Iwata, Yasutoshi Ida, Yasuhiro Fujiwara. In NeurIPS, 2022. [paper] [code]
[62] Few-Shot Non-Parametric Learning with Deep Latent Variable Model. Zhiying Jiang, Yiqin Dai, Ji Xin, Ming Li, Jimmy Lin. In NeurIPS, 2022. [paper] [code]
[63] Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs. Ruijie Wang, Zheng Li, Dachun Sun, Shengzhong Liu, Jinning Li, Bing Yin, Tarek Abdelzaher. In NeurIPS, 2022. [paper] [code]
[64] Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport. Dandan Guo, Long Tian, He Zhao, Mingyuan Zhou, Hongyuan Zha. In NeurIPS, 2022. [paper] [code]
[65] Flamingo: a Visual Language Model for Few-Shot Learning. Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andrew Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, Karen Simonyan. In NeurIPS, 2022. [paper] [code]
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[1] Learning to Compare: Relation Network for Few-Shot Learning. Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H.S. Torr, Timothy M. Hospedales. In CVPR, 2018. [paper] [code]
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[1] Few-Shot Object Recognition from Machine-Labeled Web Images. Zhongwen Xu, Linchao Zhu, Yi Yang. In CVPR, 2017. [paper] [code]
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[1] Learning feed-forward one-shot learners. Luca Bertinetto, João F. Henriques, Jack Valmadre, Philip H. S. Torr, Andrea Vedaldi. In NIPS, 2016. [paper] [code]
[2] Matching Networks for One Shot Learning. Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra. In NIPS, 2016. [paper] [code]
[1] One Shot Learning via Compositions of Meaningful Patches. Alex Wong, Alan L. Yuille. In ICCV, 2015. [paper] [code]