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Found 10 papers, 5 papers with code
Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification
Conventional few-shot learning methods however cannot be naively adopted for this fine-grained setting -- a quick pilot study reveals that they in fact push for the opposite (i. e., lower inter-class variations and higher intra-class variations).
Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments
Few-shot image classification is a challenging problem that aims to achieve the human level of recognition based only on a small number of training images.
TLRM: Task-level Relation Module for GNN-based Few-Shot Learning
We consider this method of measuring relation of samples only models the sample-to-sample relation, while neglects the specificity of different tasks.
BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification
Motivated by this, we propose a so-called \textit{Bi-Similarity Network} (\textit{BSNet}) that consists of a single embedding module and a bi-similarity module of two similarity measures.
CC-Loss: Channel Correlation Loss For Image Classification
The loss function is a key component in deep learning models.
ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification
Despite achieving state-of-the-art performance, deep learning methods generally require a large amount of labeled data during training and may suffer from overfitting when the sample size is small.
A Concise Review of Recent Few-shot Meta-learning Methods
Few-shot meta-learning has been recently reviving with expectations to mimic humanity's fast adaption to new concepts based on prior knowledge.
OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer
A deep neural network of multiple nonlinear layers forms a large function space, which can easily lead to overfitting when it encounters small-sample data.
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
The proposed loss function, termed as mutual-channel loss (MC-Loss), consists of two channel-specific components: a discriminality component and a diversity component.
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Fine-Grained Image Classification
on FGVC Aircraft
Channel Max Pooling Layer for Fine-Grained Vehicle Classification
Experimental results on two fine-grained vehicle datasets, the Stanford Cars-196 dataset and the Comp Cars dataset, demonstrate that the proposed layer could improve classification accuracies of deep neural networks on fine-grained vehicle classification in the situation that a massive of parameters are reduced.
