Meta-learning approaches for few-shot learning: A survey of recent advances
Despite its astounding success in learning deeper multi-dimensional data, the performance
of deep learning declines on new unseen tasks mainly due to its focus on same-distribution …
of deep learning declines on new unseen tasks mainly due to its focus on same-distribution …
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 …
Hierarchical graph neural networks for few-shot learning
Recent graph neural network (GNN) based methods for few-shot learning (FSL) represent
the samples of interest as a fully-connected graph and conduct reasoning on the nodes …
the samples of interest as a fully-connected graph and conduct reasoning on the nodes …
Boosting few-shot fine-grained recognition with background suppression and foreground alignment
Few-shot fine-grained recognition (FS-FGR) aims to recognize novel fine-grained categories
with the help of limited available samples. Undoubtedly, this task inherits the main …
with the help of limited available samples. Undoubtedly, this task inherits the main …
AP-CNN: Weakly supervised attention pyramid convolutional neural network for fine-grained visual classification
Few-shot named entity recognition: Definition, taxonomy and research directions
Recent years have seen an exponential growth (+ 98% in 2022 wrt the previous year) of the
number of research articles in the few-shot learning field, which aims at training machine …
number of research articles in the few-shot learning field, which aims at training machine …
Improving fine-grained visual recognition in low data regimes via self-boosting attention mechanism
The challenge of fine-grained visual recognition often lies in discovering the key
discriminative regions. While such regions can be automatically identified from a large-scale …
discriminative regions. While such regions can be automatically identified from a large-scale …
Variational feature disentangling for fine-grained few-shot classification
Data augmentation is an intuitive step towards solving the problem of few-shot classification.
However, ensuring both discriminability and diversity in the augmented samples is …
However, ensuring both discriminability and diversity in the augmented samples is …
Multi-level second-order few-shot learning
We propose a Multi-level Second-order (MlSo) few-shot learning network for supervised or
unsupervised few-shot image classification and few-shot action recognition. We leverage so …
unsupervised few-shot image classification and few-shot action recognition. We leverage so …