A list of few-shot learning papers.
[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] Learning to Obstruct Few-Shot Image Classification over Restricted Classes. Amber Yijia Zheng, Chiao-An Yang, Raymond A. Yeh. In ECCV, 2024. [paper] [code]
[14] CLOSER: Towards Better Representation Learning for Few-Shot Class-Incremental Learning. Junghun Oh, Sungyong Baik, Kyoung Mu Lee. In ECCV, 2024. [paper] [code]
[15] Semantic-guided Robustness Tuning for Few-Shot Transfer Across Extreme Domain Shift. kangyu xiao, Zilei Wang, junjie li. In ECCV, 2024. [paper] [code]
[16] On the Approximation Risk of Few-Shot Class-Incremental Learning. Xuan Wang, Zhong Ji, Xiyao Liu, Yanwei Pang, Jungong Han. In ECCV, 2024. [paper] [code]
[17] Rethinking Few-shot Class-incremental Learning: Learning from Yourself. Yu-Ming Tang, Yi-Xing Peng, Jingke Meng, Wei-Shi Zheng. In ECCV, 2024. [paper] [code]
[18] Benchmarking Spurious Bias in Few-Shot Image Classifiers. Guangtao Zheng, Wenqian Ye, Aidong Zhang. In ECCV, 2024. [paper] [code]
[19] Few-Shot Class Incremental Learning with Attention-Aware Self-Adaptive Prompt. Chenxi Liu, Zhenyi Wang, Tianyi Xiong, Ruibo Chen, Yihan Wu, Junfeng Guo, Heng Huang. In ECCV, 2024. [paper] [code]
[20] An Efficient Memory Module for Graph Few-Shot Class-Incremental Learning. Dong Li, Aijia Zhang, Junqi Gao, Biqing Qi. In NeurIPS, 2024. [paper] [code]
[21] Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning. Fei Zhou, Peng Wang, Lei Zhang, Zhenghua Chen, Wei Wei, Chen Ding, Guosheng Lin, Yanning Zhang. In NeurIPS, 2024. [paper] [code]
[22] D2R2: Diffusion-based Representation with Random Distance Matching for Tabular Few-shot Learning. Ruoxue Liu, Linjiajie Fang, Wenjia Wang, Bingyi Jing. In NeurIPS, 2024. [paper] [code]
[23] Generate Universal Adversarial Perturbations for Few-Shot Learning. Yiman Hu, Yixiong Zou, Ruixuan Li, Yuhua Li. In NeurIPS, 2024. [paper] [code]
[24] Attention Temperature Matters in ViT-Based Cross-Domain Few-Shot Learning. Yixiong Zou, Ran Ma, Yuhua Li, Ruixuan Li. In NeurIPS, 2024. [paper] [code]
[25] A Closer Look at the CLS Token for Cross-Domain Few-Shot Learning. Yixiong Zou, Shuai Yi, Yuhua Li, Ruixuan Li. In NeurIPS, 2024. [paper] [code]
[26] Mind the Gap Between Prototypes and Images in Cross-domain Finetuning. Hongduan Tian, Feng Liu, Zhanke Zhou, Tongliang Liu, Chengqi Zhang, Bo Han. In NeurIPS, 2024. [paper] [code]
[27] Neural Fine-Tuning Search for Few-Shot Learning. Panagiotis Eustratiadis, Łukasz Dudziak, Da Li, Timothy Hospedales. In ICLR, 2024. [paper] [code]
[28] A Hierarchical Bayesian Model for Deep Few-Shot Meta Learning. Minyoung Kim, Timothy Hospedales. In ICLR, 2024. [paper] [code]
[29] BECLR: Batch Enhanced Contrastive Few-Shot Learning. Stylianos Poulakakis-Daktylidis, Hadi Jamali-Rad. In ICLR, 2024. [paper] [code]
[30] MetaCoCo: A New Few-Shot Classification Benchmark with Spurious Correlation. Min Zhang, Haoxuan Li, Fei Wu, Kun Kuang. In ICLR, 2024. [paper] [code]
[31] 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]
[32] 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] 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]
[29] 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]
[30] 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]
[31] 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]
[32] 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]
[33] 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]
[34] Focus Your Attention when Few-Shot Classification. Haoqing Wang, Shibo Jie, Zhihong Deng. In NeurIPS, 2023. [paper] [code]
[35] 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]
[36] Meta-AdaM: An Meta-Learned Adaptive Optimizer with Momentum for Few-Shot Learning. Siyuan Sun, Hongyang Gao. In NeurIPS, 2023. [paper] [code]
[37] Alignment with human representations supports robust few-shot learning. Ilia Sucholutsky, Thomas L. Griffiths. In NeurIPS, 2023. [paper] [code]
[38] Unsupervised Meta-learning via Few-shot Pseudo-supervised Contrastive Learning. Huiwon Jang, Hankook Lee, Jinwoo Shin. In ICLR, 2023. [paper] [code]
[39] 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]
[40] 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]
[41] Progressive Mix-Up for Few-Shot Supervised Multi-Source Domain Transfer. Ronghang Zhu, Ronghang Zhu, Xiang Yu, Sheng Li. In ICLR, 2023. [paper] [code]
[42] Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning. Ivona Najdenkoska, Xiantong Zhen, Marcel Worring. In ICLR, 2023. [paper] [code]
[43] Contrastive Meta-Learning for Partially Observable Few-Shot Learning. Adam Jelley, Amos Storkey, Antreas Antoniou, Sam Devlin. In ICLR, 2023. [paper] [code]
[44] 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]
[45] 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]
[46] 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]
[47] 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]
[48] 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]
[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] 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]
[25] 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]
[26] Self-Supervision Can Be a Good Few-Shot Learner. Yuning Lu, Liangjian Wen, Jianzhuang Liu, Yajing Liu, Xinmei Tian. In ECCV, 2022. [paper] [code]
[27] 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]
[28] Adversarial Feature Augmentation for Cross-domain Few-shot Classification. Yanxu Hu, Andy J. Ma. In ECCV, 2022. [paper] [code]
[29] Worst Case Matters for Few-Shot Recognition. Minghao Fu, Yun-Hao Cao, Jianxin Wu. In ECCV, 2022. [paper] [code]
[30] 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]
[31] 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]
[32] Few-Shot Classification with Contrastive Learning. Zhanyuan Yang, Jinghua Wang, Yingying Zhu. In ECCV, 2022. [paper] [code]
[33] Coarse-To-Fine Incremental Few-Shot Learning. Xiang Xiang, Yuwen Tan, Qian Wan, Jing Ma. In ECCV, 2022. [paper] [code]
[34] 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]
[35] 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]
[36] 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]
[37] Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation. Min Zhang, Siteng Huang, Wenbin Li, Donglin Wang. In ECCV, 2022. [paper] [code]
[38] S3C: Self-Supervised Stochastic Classifiers for Few-Shot Class-Incremental Learning. Jayateja Kalla, Soma Biswas. In ECCV, 2022. [paper] [code]
[39] 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]
[40] 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]
[41] Kernel Relative-prototype Spectral Filtering for Few-shot Learning. Tao Zhang, Wu Huang. In ECCV, 2022. [paper] [code]
[42] 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]
[43] 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]
[44] 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]
[45] FeLMi: Few shot Learning with hard Mixup. Aniket Roy, Anshul Shah, Ketul Shah, Prithviraj Dhar, Anoop Cherian, Rama Chellappa. In NeurIPS, 2022. [paper] [code]
[46] Smoothed Embeddings for Certified Few-Shot Learning. Mikhail Pautov, Olesya Kuznetsova, Nurislam Tursynbek, Aleksandr Petiushko, Ivan Oseledets. In NeurIPS, 2022. [paper] [code]
[47] 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]
[48] Few-shot Relational Reasoning via Connection Subgraph Pretraining. Qian Huang, Hongyu Ren, Jure Leskovec. In NeurIPS, 2022. [paper] [code]
[49] Graph Few-shot Learning with Task-specific Structures. Song Wang, Chen Chen, Jundong Li. In NeurIPS, 2022. [paper] [code]
[50] Few-shot Learning for Feature Selection with Hilbert-Schmidt Independence Criterion. Atsutoshi Kumagai, Tomoharu Iwata, Yasutoshi Ida, Yasuhiro Fujiwara. In NeurIPS, 2022. [paper] [code]
[51] 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]
[52] 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]
[53] 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]
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[1] One Shot Learning via Compositions of Meaningful Patches. Alex Wong, Alan L. Yuille. In ICCV, 2015. [paper] [code]