A survey on graph neural networks and graph transformers in computer vision: A task-oriented perspective
Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …
A survey of graph neural networks in various learning paradigms: methods, applications, and challenges
In the last decade, deep learning has reinvigorated the machine learning field. It has solved
many problems in computer vision, speech recognition, natural language processing, and …
many problems in computer vision, speech recognition, natural language processing, and …
Few-shot class-incremental learning via class-aware bilateral distillation
Abstract Few-Shot Class-Incremental Learning (FSCIL) aims to continually learn novel
classes based on only few training samples, which poses a more challenging task than the …
classes based on only few training samples, which poses a more challenging task than the …
Global-and local-aware feature augmentation with semantic orthogonality for few-shot image classification
As for few-shot image classification, recently, some works revisit the standard transfer
learning paradigm, ie, pre-training and fine-tuning, and have achieved some success …
learning paradigm, ie, pre-training and fine-tuning, and have achieved some success …
Counterfactual generation framework for few-shot learning
Few-shot learning (FSL) that aims to recognize novel classes with few labeled samples is
troubled by its data scarcity. Though recent works tackle FSL with data augmentation-based …
troubled by its data scarcity. Though recent works tackle FSL with data augmentation-based …
Improved continually evolved classifiers for few-shot class-incremental learning
Few-shot class-incremental learning (FSCIL) aims to continually learn new classes using a
few samples while not forgetting the old classes. The scarcity of new training data will …
few samples while not forgetting the old classes. The scarcity of new training data will …
A survey of data-efficient graph learning
Graph-structured data, prevalent in domains ranging from social networks to biochemical
analysis, serve as the foundation for diverse real-world systems. While graph neural …
analysis, serve as the foundation for diverse real-world systems. While graph neural …
Query-specific embedding co-adaptation improve few-shot image classification
W Fu, L Zhou, J Chen - IEEE Signal Processing Letters, 2023 - ieeexplore.ieee.org
Few-Shot Image Classification (FSIC) aims to identify unseen categories by a limited
number of instances. Recently, some metric-based methods have attempted to generate …
number of instances. Recently, some metric-based methods have attempted to generate …
Hierarchical knowledge propagation and distillation for few-shot learning
Recent research efforts on Few-Shot Learning (FSL) have achieved extensive progress.
However, the existing efforts primarily focus on the transductive setting of FSL, which is …
However, the existing efforts primarily focus on the transductive setting of FSL, which is …
Less is more: A closer look at semantic-based few-shot learning
Few-shot Learning (FSL) aims to learn and distinguish new categories from a scant number
of available samples, presenting a significant challenge in the realm of deep learning …
of available samples, presenting a significant challenge in the realm of deep learning …