A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities
Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …
Multimodality in meta-learning: A comprehensive survey
Meta-learning has gained wide popularity as a training framework that is more data-efficient
than traditional machine learning methods. However, its generalization ability in complex …
than traditional machine learning methods. However, its generalization ability in complex …
Knowledge-guided semantic transfer network for few-shot image recognition
Deep learning-based models have been shown to outperform human beings in many
computer vision tasks with massive available labeled training data in learning. However …
computer vision tasks with massive available labeled training data in learning. However …
Free lunch for few-shot learning: Distribution calibration
Learning from a limited number of samples is challenging since the learned model can
easily become overfitted based on the biased distribution formed by only a few training …
easily become overfitted based on the biased distribution formed by only a few training …
Learning attention-guided pyramidal features for few-shot fine-grained recognition
Few-shot fine-grained recognition (FS-FGR) aims to distinguish several highly similar
objects from different sub-categories with limited supervision. However, traditional few-shot …
objects from different sub-categories with limited supervision. However, traditional few-shot …
Registration based few-shot anomaly detection
This paper considers few-shot anomaly detection (FSAD), a practical yet under-studied
setting for anomaly detection (AD), where only a limited number of normal images are …
setting for anomaly detection (AD), where only a limited number of normal images are …
Semantic relation reasoning for shot-stable few-shot object detection
Few-shot object detection is an imperative and long-lasting problem due to the inherent long-
tail distribution of real-world data. Its performance is largely affected by the data scarcity of …
tail distribution of real-world data. Its performance is largely affected by the data scarcity of …
Adversarial feature hallucination networks for few-shot learning
The recent flourish of deep learning in various tasks is largely accredited to the rich and
accessible labeled data. Nonetheless, massive supervision remains a luxury for many real …
accessible labeled data. Nonetheless, massive supervision remains a luxury for many real …
A broader study of cross-domain few-shot learning
Recent progress on few-shot learning largely relies on annotated data for meta-learning:
base classes sampled from the same domain as the novel classes. However, in many …
base classes sampled from the same domain as the novel classes. However, in many …
Hybrid relation guided set matching for few-shot action recognition
Current few-shot action recognition methods reach impressive performance by learning
discriminative features for each video via episodic training and designing various temporal …
discriminative features for each video via episodic training and designing various temporal …