{"list":"other","total":69,"rows":[{"id":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"id":"1409.4842","title":"Going Deeper with Convolutions","created":"2014-09-17","cited_by_count":4241},{"id":"1703.05175","title":"Prototypical Networks for Few-shot Learning","created":"2017-03-15","cited_by_count":3015},{"id":"1606.04080","title":"Matching Networks for One Shot Learning","created":"2016-06-13","cited_by_count":2255},{"id":"1711.06025","title":"Learning to Compare: Relation Network for Few-Shot Learning","created":"2017-11-16","cited_by_count":1143},{"id":"1904.05160","title":"Large-Scale Long-Tailed Recognition in an Open World","created":"2019-04-10","cited_by_count":705},{"id":"1904.04232","title":"A Closer Look at Few-shot Classification","created":"2019-04-08","cited_by_count":684},{"id":"2001.04193","title":"Deep Learning for Person Re-identification: A Survey and Outlook","created":"2020-01-13","cited_by_count":549},{"id":"1904.05046","title":"Generalizing from a Few Examples: A Survey on Few-Shot Learning","created":"2019-04-10","cited_by_count":463},{"id":"1803.00676","title":"Meta-Learning for Semi-Supervised Few-Shot Classification","created":"2018-03-02","cited_by_count":456},{"id":"1805.10123","title":"TADAM: Task dependent adaptive metric for improved few-shot learning","created":"2018-05-23","cited_by_count":408},{"id":"1805.08136","title":"Meta-learning with differentiable closed-form solvers","created":"2018-05-21","cited_by_count":397},{"id":"1804.09458","title":"Dynamic Few-Shot Visual Learning without Forgetting","created":"2018-04-25","cited_by_count":385},{"id":"1712.04621","title":"The Effectiveness of Data Augmentation in Image Classification using Deep Learning","created":"2017-12-13","cited_by_count":370},{"id":"1512.07108","title":"Recent Advances in Convolutional Neural Networks","created":"2015-12-22","cited_by_count":360},{"id":"1711.04043","title":"Few-Shot Learning with Graph Neural Networks","created":"2017-11-10","cited_by_count":324},{"id":"1903.03096","title":"Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples","created":"2019-03-07","cited_by_count":315},{"id":"1812.02391","title":"Meta-Transfer Learning for Few-Shot Learning","created":"2018-12-06","cited_by_count":306},{"id":"1812.03664","title":"Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions","created":"2018-12-10","cited_by_count":294},{"id":"1612.07828","title":"Learning from Simulated and Unsupervised Images through Adversarial Training","created":"2016-12-22","cited_by_count":288},{"id":"1711.04340","title":"Data Augmentation Generative Adversarial Networks","created":"2017-11-12","cited_by_count":278},{"id":"1605.07723","title":"Data Programming: Creating Large Training Sets, Quickly","created":"2016-05-25","cited_by_count":246},{"id":"2008.05659","title":"What Should Not Be Contrastive in Contrastive Learning","created":"2020-08-13","cited_by_count":226},{"id":"1805.10002","title":"Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning","created":"2018-05-25","cited_by_count":211},{"id":"2001.08735","title":"Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation","created":"2020-01-23","cited_by_count":185},{"id":"1801.05401","title":"Low-Shot Learning from Imaginary Data","created":"2018-01-16","cited_by_count":181},{"id":"2007.11498","title":"CrossTransformers: spatially-aware few-shot transfer","created":"2020-07-22","cited_by_count":174},{"id":"1906.05186","title":"Boosting Few-Shot Visual Learning with Self-Supervision","created":"2019-06-12","cited_by_count":140},{"id":"1906.07697","title":"Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes","created":"2019-06-18","cited_by_count":131},{"id":"2003.12060","title":"Negative Margin Matters: Understanding Margin in Few-shot Classification","created":"2020-03-26","cited_by_count":129},{"id":"1503.03167","title":"Deep Convolutional Inverse Graphics Network","created":"2015-03-11","cited_by_count":121},{"id":"1903.12290","title":"Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning","created":"2019-03-28","cited_by_count":121},{"id":"1912.03432","title":"Improved Few-Shot Visual Classification","created":"2019-12-07","cited_by_count":115},{"id":"1905.01436","title":"Edge-labeling Graph Neural Network for Few-shot Learning","created":"2019-05-04","cited_by_count":114},{"id":"1905.11116","title":"Finding Task-Relevant Features for Few-Shot Learning by Category Traversal","created":"2019-05-27","cited_by_count":101},{"id":"1902.04552","title":"Infinite Mixture Prototypes for Few-Shot Learning","created":"2019-02-12","cited_by_count":99},{"id":"1709.01643","title":"Learning to Compose Domain-Specific Transformations for Data Augmentation","created":"2017-09-06","cited_by_count":91},{"id":"1905.06549","title":"TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning","created":"2019-05-16","cited_by_count":91},{"id":"2003.14247","title":"DPGN: Distribution Propagation Graph Network for Few-shot Learning","created":"2020-03-31","cited_by_count":79},{"id":"2009.09450","title":"Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition","created":"2020-09-20","cited_by_count":69},{"id":"1910.02224","title":"Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning","created":"2019-10-05","cited_by_count":67},{"id":"1905.04398","title":"Few-Shot Learning with Embedded Class Models and Shot-Free Meta Training","created":"2019-05-10","cited_by_count":57},{"id":"2103.13841","title":"Universal Representation Learning from Multiple Domains for Few-shot Classification","created":"2021-03-25","cited_by_count":56},{"id":"1904.03472","title":"Few-Shot Learning via Saliency-guided Hallucination of Samples","created":"2019-04-06","cited_by_count":49},{"id":"2006.11184","title":"Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates","created":"2020-06-19","cited_by_count":49},{"id":"1908.05257","title":"Few-Shot Learning with Global Class Representations","created":"2019-08-14","cited_by_count":44},{"id":"2006.13276","title":"Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images","created":"2020-06-16","cited_by_count":43},{"id":"1803.00130","title":"Ring loss: Convex Feature Normalization for Face Recognition","created":"2018-02-28","cited_by_count":39},{"id":"1908.01313","title":"Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification","created":"2019-08-04","cited_by_count":36},{"id":"2001.03919","title":"Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning","created":"2020-01-12","cited_by_count":31},{"id":"2003.08561","title":"XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning","created":"2020-03-19","cited_by_count":31},{"id":"2011.14311","title":"BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification","created":"2020-11-29","cited_by_count":30},{"id":"1706.08653","title":"A Unified approach for Conventional Zero-shot, Generalized Zero-shot and Few-shot Learning","created":"2017-06-27","cited_by_count":28},{"id":"1805.04288","title":"Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples","created":"2018-05-11","cited_by_count":27},{"id":"1905.06331","title":"LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning","created":"2019-05-15","cited_by_count":27},{"id":"1909.04332","title":"PARN: Position-Aware Relation Networks for Few-Shot Learning","created":"2019-09-10","cited_by_count":27},{"id":"1911.09579","title":"Knowledge Graph Transfer Network for Few-Shot Recognition","created":"2019-11-21","cited_by_count":20},{"id":"2007.08398","title":"Semi-Siamese Training for Shallow Face Learning","created":"2020-07-16","cited_by_count":19},{"id":"1808.04572","title":"Small Sample Learning in Big Data Era","created":"2018-08-14","cited_by_count":18},{"id":"1912.04973","title":"A Two-Stage Approach to Few-Shot Learning for Image Recognition","created":"2019-12-10","cited_by_count":18},{"id":"1912.11809","title":"Variational Metric Scaling for Metric-Based Meta-Learning","created":"2019-12-26","cited_by_count":17},{"id":"2012.13831","title":"Spatial Contrastive Learning for Few-Shot Classification","created":"2020-12-26","cited_by_count":17},{"id":"1810.08332","title":"Zero and Few Shot Learning with Semantic Feature Synthesis and Competitive Learning","created":"2018-10-19","cited_by_count":15},{"id":"2005.10953","title":"A Concise Review of Recent Few-shot Meta-learning Methods","created":"2020-05-22","cited_by_count":13},{"id":"2005.13820","title":"TOAN: Target-Oriented Alignment Network for Fine-Grained Image Categorization with Few Labeled Samples","created":"2020-05-28","cited_by_count":13},{"id":"2012.10844","title":"PTN: A Poisson Transfer Network for Semi-supervised Few-shot Learning","created":"2020-12-20","cited_by_count":9}],"next_cursor":null}