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Few-Shot Learning

1013 directly classified papers

Papers per year

Papers

ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-Shot Learning CVPR 2021
Learning To Count Everything CVPR 2021
Prototypical Cross-Domain Self-Supervised Learning for Few-Shot Unsupervised Domain Adaptation CVPR 2021
Few-Shot Learning via Feature Hallucination With Variational Inference WACV 2021
Want To Reduce Labeling Cost? GPT-3 Can Help EMNLP 2021
FLAR: A Unified Prototype Framework for Few-Sample Lifelong Active Recognition ICCV 2021
Energy-Based Learning for Scene Graph Generation CVPR 2021
On the Importance of Distractors for Few-Shot Classification ICCV 2021
Efficient Contrastive Learning via Novel Data Augmentation and Curriculum Learning EMNLP 2021
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems EMNLP 2021
Learning Prototype Representations Across Few-Shot Tasks for Event Detection EMNLP 2021
Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking? IJCNLP 2021
AugNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation IJCNLP 2021
Video Pose Distillation for Few-Shot, Fine-Grained Sports Action Recognition ICCV 2021
Partner-Assisted Learning for Few-Shot Image Classification ICCV 2021
DreamDrug - A crowdsourced NER dataset for detecting drugs in darknet markets EMNLP 2021
Few-Shot Intent Classification by Gauging Entailment Relationship Between Utterance and Semantic Label EMNLP 2021
Few-shot Learning for Slot Tagging with Attentive Relational Network EACL 2021
Few-Shot Semantic Parsing for New Predicates EACL 2021
Language Models for Lexical Inference in Context EACL 2021
Few-shot learning through contextual data augmentation EACL 2021
FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary EACL 2021
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference EACL 2021
Few-shot and Zero-shot Approaches to Legal Text Classification: A Case Study in the Financial Sector EMNLP 2021
Language Models are Few-shot Multilingual Learners EMNLP 2021