Fetching the paper…
Reading the bibliography…
Few-shot learning (FSL) has emerged as an effective learning method and shows great potential.
How many memory systems are there?
Endel Tulving · 1985
Earlier work this paper cites.
Episodic memory: From mind to brain
Endel Tulving · 2002
Earlier work this paper cites.
A visual attention model for adapting images on small displays
Li-Qun Chen, Xing Xie, Xin Fan, Wei-Ying Ma, Hong-Jiang Zhang, and He-Qin Zhou · 2003
Earlier work this paper cites.
Learning a distance metric from relative comparisons
Matthew Schultz and Thorsten Joachims · 2004
Earlier work this paper cites.
Distance metric learning: A comprehensive survey
Liu Yang and Rong Jin · 2006
Earlier work this paper cites.
Distance metric learning for large margin nearest neighbor classification
Kilian Q Weinberger, John Blitzer, and Lawrence K Saul · 2006
Earlier work this paper cites.
Learning to detect unseen object classes by between-class attribute transfer
Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2009
Earlier work this paper cites.
On the similarity metric and the distance metric
Shihyen Chen, Bin Ma, and Kaizhong Zhang · 2009
Earlier work this paper cites.
Sun database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba · 2010
Earlier work this paper cites.
The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
Earlier work this paper cites.
One-shot learning with a hierarchical nonparametric bayesian model
Ruslan Salakhutdinov, Joshua Tenenbaum, and Antonio Torralba · 2012
Earlier work this paper cites.
Autoencoder for words
Cheng-Yuan Liou, Wei-Chen Cheng, Jiun-Wei Liou, and Daw-Ran Liou · 2014
Earlier work this paper cites.
Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
Earlier work this paper cites.
Xavier Bouthillier, Kishore Konda, Pascal Vincent, and Roland Memisevic · 2015
Earlier work this paper cites.
Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, Ruslan Salakhutdinov, et al · 2015
Earlier work this paper cites.
Deep metric learning using triplet network
Elad Hoffer and Nir Ailon · 2015
Earlier work this paper cites.
Deep visual-semantic alignments for generating image descriptions
Andrej Karpathy and Li Fei-Fei · 2015
Earlier work this paper cites.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
Earlier work this paper cites.
Using deep learning for image-based plant disease detection
Sharada P Mohanty, David P Hughes, and Marcel Salathé · 2016
Earlier work this paper cites.
One-shot learning of scene locations via feature trajectory transfer
Roland Kwitt, Sebastian Hegenbart, and Marc Niethammer · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas · 2016
Earlier work this paper cites.
Learning deep representations of fine-grained visual descriptions
Scott Reed, Zeynep Akata, Honglak Lee, and Bernt Schiele · 2016
Earlier work this paper cites.
Mark Woodward and Chelsea Finn · 2017
Earlier work this paper cites.
Generative adversarial residual pairwise networks for one shot learning
Akshay Mehrotra and Ambedkar Dukkipati · 2017
Earlier work this paper cites.
Semantic autoencoder for zero-shot learning
Elyor Kodirov, Tao Xiang, and Shaogang Gong · 2017
Earlier work this paper cites.
One-shot learning for semantic segmentation
Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, and Byron Boots · 2017
Earlier work this paper cites.
Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers · 2017
Earlier work this paper cites.
Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
Earlier work this paper cites.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
Earlier work this paper cites.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Earlier work this paper cites.
Universal representations: The missing link between faces, text, planktons, and cat breeds
Hakan Bilen and Andrea Vedaldi · 2017
Earlier work this paper cites.
Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Earlier work this paper cites.
Meta-sgd: Learning to learn quickly for few-shot learning
Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li · 2017
Earlier work this paper cites.
Learned optimizers that scale and generalize
Olga Wichrowska, Niru Maheswaranathan, Matthew W Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando Freitas, and Jascha Sohl-Dickstein · 2017
Earlier work this paper cites.
Beyond triplet loss: a deep quadruplet network for person re-identification
Weihua Chen, Xiaotang Chen, Jianguo Zhang, and Kaiqi Huang · 2017
Earlier work this paper cites.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S Zemel · 2017
Earlier work this paper cites.
Few-shot learning with graph neural networks
Victor Garcia and Joan Bruna · 2017
Earlier work this paper cites.
Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris N Metaxas · 2017
Earlier work this paper cites.
Link the head to the” beak”: Zero shot learning from noisy text description at part precision
Mohamed Elhoseiny, Yizhe Zhu, Han Zhang, and Ahmed Elgammal · 2017
Earlier work this paper cites.
Small sample learning in big data era
Jun Shu, Zongben Xu, and Deyu Meng · 2018
Earlier work this paper cites.
Repmet: Representative-based metric learning for classification and one-shot object detection
Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder, Sharathchandra Pankanti, Rogerio Feris, Abhishek Kumar, Raja Giries, and Alex M Bronstein · 2018
Earlier work this paper cites.
Feature generating networks for zero-shot learning
Yongqin Xian, Tobias Lorenz, Bernt Schiele, and Zeynep Akata · 2018
Earlier work this paper cites.
Joaquin Vanschoren · 2018
Earlier work this paper cites.
Paris-lille-3d: A large and high-quality ground-truth urban point cloud dataset for automatic segmentation and classification
Xavier Roynard, Jean-Emmanuel Deschaud, and François Goulette · 2018
Earlier work this paper cites.
Modeling uncertainty with hedged instance embedding
Seong Joon Oh, Kevin Murphy, Jiyan Pan, Joseph Roth, Florian Schroff, and Andrew Gallagher · 2018
Earlier work this paper cites.
Low-shot learning via covariance-preserving adversarial augmentation networks
Hang Gao, Zheng Shou, Alireza Zareian, Hanwang Zhang, and Shih-Fu Chang · 2018
Earlier work this paper cites.
Semantic feature augmentation in few-shot learning
Zitian Chen, Yanwei Fu, Yinda Zhang, Yu-Gang Jiang, Xiangyang Xue, and Leonid Sigal · 2018
Earlier work this paper cites.
Data augmentation by pairing samples for images classification
Hiroshi Inoue · 2018
Earlier work this paper cites.
Delta-encoder: an effective sample synthesis method for few-shot object recognition
Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder, Rogerio Feris, Abhishek Kumar, Raja Giryes, and Alex M Bronstein · 2018
Earlier work this paper cites.
Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
A study on cnn transfer learning for image classification
Mahbub Hussain, Jordan J Bird, and Diego R Faria · 2018
Earlier work this paper cites.
Meta-learning with latent embedding optimization
Andrei A Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell · 2018
Earlier work this paper cites.
On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
Earlier work this paper cites.
Rein Houthooft, Richard Y Chen, Phillip Isola, Bradly C Stadie, Filip Wolski, Jonathan Ho, and Pieter Abbeel · 2018
Earlier work this paper cites.
Efficient neural architecture search via parameters sharing
Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean · 2018
Earlier work this paper cites.
Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
Earlier work this paper cites.
Understanding and simplifying one-shot architecture search
Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, and Quoc Le · 2018
Earlier work this paper cites.
Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales · 2018
Earlier work this paper cites.
Chatpainter: Improving text to image generation using dialogue
Shikhar Sharma, Dendi Suhubdy, Vincent Michalski, Samira Ebrahimi Kahou, and Yoshua Bengio · 2018
Earlier work this paper cites.
A generative adversarial approach for zero-shot learning from noisy texts
Yizhe Zhu, Mohamed Elhoseiny, Bingchen Liu, Xi Peng, and Ahmed Elgammal · 2018
Earlier work this paper cites.
One-shot instance segmentation
Claudio Michaelis, Ivan Ustyuzhaninov, Matthias Bethge, and Alexander S Ecker · 2018
Earlier work this paper cites.
A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang · 2019
Earlier work this paper cites.
Adaptive cross-modal few-shot learning
Chen Xing, Negar Rostamzadeh, Boris Oreshkin, and Pedro O O Pinheiro · 2019
Earlier work this paper cites.
Kg-bert: Bert for knowledge graph completion
Liang Yao, Chengsheng Mao, and Yuan Luo · 2019
Earlier work this paper cites.
Image deformation meta-networks for one-shot learning
Zitian Chen, Yanwei Fu, Yu-Xiong Wang, Lin Ma, Wei Liu, and Martial Hebert · 2019
Earlier work this paper cites.
Meta-dataset: A dataset of datasets for learning to learn from few examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, et al · 2019
Earlier work this paper cites.
Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
Earlier work this paper cites.
Noel Codella, Veronica Rotemberg, Philipp Tschandl, M Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, et al · 2019
Earlier work this paper cites.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
Cited alongside, same era.
Spot and learn: A maximum-entropy patch sampler for few-shot image classification
Wen-Hsuan Chu, Yu-Jhe Li, Jing-Cheng Chang, and Yu-Chiang Frank Wang · 2019
Cited alongside, same era.
Few-shot learning via saliency-guided hallucination of samples
Hongguang Zhang, Jing Zhang, and Piotr Koniusz · 2019
Cited alongside, same era.
Laso: Label-set operations networks for multi-label few-shot learning
Amit Alfassy, Leonid Karlinsky, Amit Aides, Joseph Shtok, Sivan Harary, Rogerio Feris, Raja Giryes, and Alex M Bronstein · 2019
Cited alongside, same era.
Multi-level semantic feature augmentation for one-shot learning
Zitian Chen, Yanwei Fu, Yinda Zhang, Yu-Gang Jiang, Xiangyang Xue, and Leonid Sigal · 2019
Cited alongside, same era.
Graph few-shot learning via knowledge transfer
Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh Chawla, and Zhenhui Li · 2020
Later among the works it cites.
Graph embedding relation network for few-shot learning
Zhen Liu, Yitong Xia, and Baochang Zhang · 2020
Later among the works it cites.
Improved few-shot visual classification
Peyman Bateni, Raghav Goyal, Vaden Masrani, Frank Wood, and Leonid Sigal · 2020
Later among the works it cites.
Extended few-shot learning: Exploiting existing resources for novel tasks
Reza Esfandiarpoor, Amy Pu, Mohsen Hajabdollahi, and Stephen H Bach · 2020
Later among the works it cites.
Multi-scale adaptive task attention network for few-shot learning
Haoxing Chen, Huaxiong Li, Yaohui Li, and Chunlin Chen · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Junsik Kim, Tae-Hyun Oh, Seokju Lee, Fei Pan, and In So Kweon · 2019
Cited alongside, same era.
A baseline for few-shot image classification
Guneet S Dhillon, Pratik Chaudhari, Avinash Ravichandran, and Stefano Soatto · 2019
Cited alongside, same era.
Revisiting fine-tuning for few-shot learning
Akihiro Nakamura and Tatsuya Harada · 2019
Cited alongside, same era.
Dense classification and implanting for few-shot learning
Yann Lifchitz, Yannis Avrithis, Sylvaine Picard, and Andrei Bursuc · 2019
Cited alongside, same era.
Rapid learning or feature reuse? towards understanding the effectiveness of maml
Aniruddh Raghu, Maithra Raghu, Samy Bengio, and Oriol Vinyals · 2019
Cited alongside, same era.
Task agnostic meta-learning for few-shot learning
Muhammad Abdullah Jamal and Guo-Jun Qi · 2019
Cited alongside, same era.
Meta-learning with implicit gradients, 2019
Aravind Rajeswaran, Chelsea Finn, Sham Kakade, and Sergey Levine · 2019
Cited alongside, same era.
Bowen Wang, Liangzhi Li, Manisha Verma, Yuta Nakashima, Ryo Kawasaki, and Hajime Nagahara · 2020
Later among the works it cites.
Gpu-based self-organizing maps for post-labeled few-shot unsupervised learning
Lyes Khacef, Vincent Gripon, and Benoît Miramond · 2020
Later among the works it cites.
Region comparison network for interpretable few-shot image classification
Zhiyu Xue, Lixin Duan, Wen Li, Lin Chen, and Jiebo Luo · 2020
Later among the works it cites.
Laplacian regularized few-shot learning
Imtiaz Ziko, Jose Dolz, Eric Granger, and Ismail Ben Ayed · 2020
Later among the works it cites.
Enhancing few-shot image classification with unlabelled examples
Peyman Bateni, Jarred Barber, Jan-Willem van de Meent, and Frank Wood · 2020
Later among the works it cites.
Self-supervised knowledge distillation for few-shot learning
Jathushan Rajasegaran, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Mubarak Shah · 2020
Later among the works it cites.
Leveraging the feature distribution in transfer-based few-shot learning
Yuqing Hu, Vincent Gripon, and Stéphane Pateux · 2020
Later among the works it cites.
Adaptive subspaces for few-shot learning
Christian Simon, Piotr Koniusz, Richard Nock, and Mehrtash Harandi · 2020
Later among the works it cites.
Boosting few-shot learning with adaptive margin loss
Aoxue Li, Weiran Huang, Xu Lan, Jiashi Feng, Zhenguo Li, and Liwei Wang · 2020
Later among the works it cites.
Empirical bayes transductive meta-learning with synthetic gradients
Shell Xu Hu, Pablo G Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil D Lawrence, and Andreas Damianou · 2020
Later among the works it cites.
Instance credibility inference for few-shot learning
Yikai Wang, Chengming Xu, Chen Liu, Li Zhang, and Yanwei Fu · 2020
Later among the works it cites.
Pac-bayesian meta-learning with implicit prior and posterior
Cuong Nguyen, Thanh-Toan Do, and Gustavo Carneiro · 2020
Later among the works it cites.
Few-shot learning as domain adaptation: Algorithm and analysis
Jiechao Guan, Zhiwu Lu, Tao Xiang, and Ji-Rong Wen · 2020
Later among the works it cites.
Metafun: Meta-learning with iterative functional updates
Jin Xu, Jean-Francois Ton, Hyunjik Kim, Adam Kosiorek, and Yee Whye Teh · 2020
Later among the works it cites.
Task augmentation by rotating for meta-learning
Jialin Liu, Fei Chao, and Chih-Min Lin · 2020
Later among the works it cites.
Bayesian meta-learning for the few-shot setting via deep kernels
Massimiliano Patacchiola, Jack Turner, Elliot J Crowley, Michael O’Boyle, and Amos Storkey · 2020
Later among the works it cites.
Charting the right manifold: Manifold mixup for few-shot learning
Puneet Mangla, Nupur Kumari, Abhishek Sinha, Mayank Singh, Balaji Krishnamurthy, and Vineeth N Balasubramanian · 2020
Later among the works it cites.
Few-shot learning via embedding adaptation with set-to-set functions
Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, and Fei Sha · 2020
Later among the works it cites.
Few-shot object detection and viewpoint estimation for objects in the wild
Yang Xiao and Renaud Marlet · 2020
Later among the works it cites.
Multi-scale positive sample refinement for few-shot object detection
Jiaxi Wu, Songtao Liu, Di Huang, and Yunhong Wang · 2020
Later among the works it cites.
Frustratingly simple few-shot object detection
Xin Wang, Thomas E Huang, Trevor Darrell, Joseph E Gonzalez, and Fisher Yu · 2020
Later among the works it cites.
Prototype mixture models for few-shot semantic segmentation
Boyu Yang, Chang Liu, Bohao Li, Jianbin Jiao, and Qixiang Ye · 2020
Later among the works it cites.
Prior guided feature enrichment network for few-shot segmentation
Zhuotao Tian, Hengshuang Zhao, Michelle Shu, Zhicheng Yang, Ruiyu Li, and Jiaya Jia · 2020
Later among the works it cites.
Part-aware prototype network for few-shot semantic segmentation
Yongfei Liu, Xiangyi Zhang, Songyang Zhang, and Xuming He · 2020
Later among the works it cites.
Fgn: Fully guided network for few-shot instance segmentation
Zhibo Fan, Jin-Gang Yu, Zhihao Liang, Jiarong Ou, Changxin Gao, Gui-Song Xia, and Yuanqing Li · 2020
Later among the works it cites.
Sg-one: Similarity guidance network for one-shot semantic segmentation
Xiaolin Zhang, Yunchao Wei, Yi Yang, and Thomas S Huang · 2020
Later among the works it cites.
A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2021
Later among the works it cites.
Future trends and challenges in next generation smart application of 5g-iot
Sakshi Painuly, Sachin Sharma, and Priya Matta · 2021
Later among the works it cites.
Improving siamese networks for one-shot learning using kernel-based activation functions
Shruti Jadon and Aditya Arcot Srinivasan · 2021
Later among the works it cites.
Learning graph embeddings for compositional zero-shot learning
Muhammad Ferjad Naeem, Yongqin Xian, Federico Tombari, and Zeynep Akata · 2021
Later among the works it cites.
Mural: Meta-learning uncertainty-aware rewards for outcome-driven reinforcement learning
Kevin Li, Abhishek Gupta, Ashwin Reddy, Vitchyr H Pong, Aurick Zhou, Justin Yu, and Sergey Levine · 2021
Later among the works it cites.
Meta-detr: Few-shot object detection via unified image-level meta-learning
Gongjie Zhang, Zhipeng Luo, Kaiwen Cui, and Shijian Lu · 2021
Later among the works it cites.
Meta-learning based incremental few-shot object detection
Meng Cheng, Hanli Wang, and Yu Long · 2021
Later among the works it cites.
Learning intact features by erasing-inpainting for few-shot classification
Junjie Li, Zilei Wang, and Xiaoming Hu · 2021
Later among the works it cites.
Adargcn: Adaptive aggregation gcn for few-shot learning
Jianhong Zhang, Manli Zhang, Zhiwu Lu, and Tao Xiang · 2021
Later among the works it cites.
Federated transfer learning based cross-domain prediction for smart manufacturing
I Kevin, Kai Wang, Xiaokang Zhou, Wei Liang, Zheng Yan, and Jinhua She · 2021
Later among the works it cites.
Partial is better than all: Revisiting fine-tuning strategy for few-shot learning
Zhiqiang Shen, Zechun Liu, Jie Qin, Marios Savvides, and Kwang-Ting Cheng · 2021
Later among the works it cites.
Few-shot learning for road object detection
Anay Majee, Kshitij Agrawal, and Anbumani Subramanian · 2021
Later among the works it cites.
Few-shot question answering by pretraining span selection
Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, and Omer Levy · 2021
Later among the works it cites.
Few-shot classification with feature map reconstruction networks
Davis Wertheimer, Luming Tang, and Bharath Hariharan · 2021
Later among the works it cites.
Explanation-guided training for cross-domain few-shot classification
Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, Yunqing Zhao, Ngai-Man Cheung, and Alexander Binder · 2021
Later among the works it cites.
Meta-fdmixup: Cross-domain few-shot learning guided by labeled target data
Yuqian Fu, Yanwei Fu, and Yu-Gang Jiang · 2021
Later among the works it cites.
Universal representation learning from multiple domains for few-shot classification
Wei-Hong Li, Xialei Liu, and Hakan Bilen · 2021
Later among the works it cites.
Reproducibility report: La-maml: Look-ahead meta learning for continual learning
Joel Joseph and Alex Gu · 2021
Later among the works it cites.
Fixed-maml for few shot classification in multilingual speech emotion recognition
Anugunj Naman and Liliana Mancini · 2021
Later among the works it cites.
Rnn/lstm with modified adam optimizer in deep learning approach for automobile spare parts demand forecasting
Kiran Kumar Chandriah and Raghavendra V Naraganahalli · 2021
Later among the works it cites.
Few-shot neural architecture search
Yiyang Zhao, Linnan Wang, Yuandong Tian, Rodrigo Fonseca, and Tian Guo · 2021
Later among the works it cites.
Hierarchical graph neural networks for few-shot learning
Cen Chen, Kenli Li, Wei Wei, Joey Tianyi Zhou, and Zeng Zeng · 2021
Later among the works it cites.
Frog-gnn: Multi-perspective aggregation based graph neural network for few-shot text classification
Shiyao Xu and Yang Xiang · 2021
Later among the works it cites.
Multi-dimensional edge features graph neural network on few-shot image classification
Chao Xiong, Wen Li, Yun Liu, and Minghui Wang · 2021
Later among the works it cites.
Scale-aware graph neural network for few-shot semantic segmentation
Guo-Sen Xie, Jie Liu, Huan Xiong, and Ling Shao · 2021
Later among the works it cites.
Multimodal prototypical networks for few-shot learning
Frederik Pahde, Mihai Puscas, Tassilo Klein, and Moin Nabi · 2021
Later among the works it cites.
Unsupervised embedding adaptation via early-stage feature reconstruction for few-shot classification
Dong Hoon Lee and Sae-Young Chung · 2021
Later among the works it cites.
Bridging multi-task learning and meta-learning: Towards efficient training and effective adaptation
Haoxiang Wang, Han Zhao, and Bo Li · 2021
Later among the works it cites.
Rich semantics improve few-shot learning
Mohamed Afham, Salman Khan, Muhammad Haris Khan, Muzammal Naseer, and Fahad Shahbaz Khan · 2021
Later among the works it cites.
Exploring complementary strengths of invariant and equivariant representations for few-shot learning
Mamshad Nayeem Rizve, Salman Khan, Fahad Shahbaz Khan, and Mubarak Shah · 2021
Later among the works it cites.
Complementing representation deficiency in few-shot image classification: A meta-learning approach
Xian Zhong, Cheng Gu, Wenxin Huang, Lin Li, Shuqin Chen, and Chia-Wen Lin · 2021
Later among the works it cites.
Self-supervised learning for few-shot image classification
Da Chen, Yuefeng Chen, Yuhong Li, Feng Mao, Yuan He, and Hui Xue · 2021
Later among the works it cites.
Fsce: Few-shot object detection via contrastive proposal encoding
Bo Sun, Banghuai Li, Shengcai Cai, Ye Yuan, and Chi Zhang · 2021
Later among the works it cites.
Semantic relation reasoning for shot-stable few-shot object detection
Chenchen Zhu, Fangyi Chen, Uzair Ahmed, Zhiqiang Shen, and Marios Savvides · 2021
Later among the works it cites.
Simpler is better: Few-shot semantic segmentation with classifier weight transformer
Zhihe Lu, Sen He, Xiatian Zhu, Li Zhang, Yi-Zhe Song, and Tao Xiang · 2021
Later among the works it cites.
Few-shot segmentation via cycle-consistent transformer
Gengwei Zhang, Guoliang Kang, Yunchao Wei, and Yi Yang · 2021
Later among the works it cites.
Hypercorrelation squeeze for few-shot segmentation
Juhong Min, Dahyun Kang, and Minsu Cho · 2021
Later among the works it cites.
Incremental few-shot instance segmentation
Dan Andrei Ganea, Bas Boom, and Ronald Poppe · 2021
Later among the works it cites.
Fapis: A few-shot anchor-free part-based instance segmenter
Khoi Nguyen and Sinisa Todorovic · 2021
Later among the works it cites.
Free lunch for few-shot learning: Distribution calibration
Shuo Yang, Lu Liu, and Min Xu · 2021
Later among the works it cites.