Fine|Grained Few|Shot Image Classification Based on Feature Dual ...

Fine-Grained Few-Shot Image Classification Based on Feature Dual ...

Fine-grained few-shot image classification is a popular research area in deep learning. The main goal is to identify subcategories within a broader category ...

Fine-Grained Few-Shot Image Classification Based on Feature Dual ...

Abstract. Fine-grained few-shot image classification is a popular research area in deep learning. The main goal is to identify subcategories ...

Fine-Grained Few-Shot Image Classification ... - Paper | Scholar-Chat

Fine-Grained Few-Shot Image Classification Based on Feature Dual Reconstruction. Shudong Liu, Wenlong Zhong, Furong Guo, ..., Boyu Gu - 2024. Abstract Fine ...

Dual Attention Networks for Few-Shot Fine-Grained Recognition

After that, both outputs of dual branches are aggregated as a holistic image embedding w.r.t. input images. By performing meta-learning, we can learn a powerful ...

[2207.08547] Few-shot Fine-grained Image Classification via Multi ...

We propose a novel few-shot fine-grained image classification network (FicNet) using multi-frequency neighborhood (MFN) and double-cross modulation (DCM).

Few-Shot Fine-Grained Image Classification - MDPI

The features of support images and query images were fed into a dual complete graph network, where a point-to-distribution aggregation strategy was applied ...

FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide ...

Unlike few-shot learning methods in natural images that can leverage the labels of each image, existing few-shot WSI classification methods only ...

CSer-Tang-hao/Awesome-Fine-Grained-Few-Shot-Learning - GitHub

... Feature Optimization Network for Few-Shot Fine-Grained Image Classification ... Fine-grained Image Classification via Multi-Frequency Neighborhood and Double ...

Self-reconstruction network for fine-grained few-shot classification

The recently proposed feature reconstruction-based approach, which reconstructs query image features ... Fine-grained few-shot classification faces the dual ...

Dual Attention Networks for Few-Shot Fine-Grained Recognition

By performing meta-learning, we can learn a powerful image embedding in such a metric space to general- ize to novel classes. Experiments on ...

Dual Attention Networks for Few-Shot Fine-Grained Recognition

After that, both outputs of dual branches are aggregated as a holistic image embedding w.r.t. input images. By performing meta-learning, we can ...

Feature fusion network based on few-shot fine-grained classification

Traditional metric learning techniques, like matching networks and relation networks, often depend on entire image features for recognition.

Dual class representation learning for few-shot image classification

Since these models cannot be properly fine-tuned on the novel classes using the few training examples, FSL methods use the features extracted by ...

Task-specific Part Discovery for Fine-grained Few-shot Classification

Zero-shot fine-grained classification by deep feature ... Few-shot fine-grained image classification via multi-frequency neighborhood and double ...

Few-Shot Image Classification Based on Cross-Dimensional ...

The algorithm uses the pre-trained model Resnet-12 to extract deep features of images and introduces the cross-dimensional interactive attention ...

Learning feature alignment and dual correlation for few‐shot image ...

Few-shot image classification is the task of classifying novel classes using extremely limited labelled samples. To perform classification ...

[PDF] Few-Shot Learning for Domain-Specific Fine-Grained Image ...

A novel few-shot fine-grained image classification network (FicNet) using multi-frequency neighborhood (MFN) and double-cross modulation (DCM) using multi ...

Revisiting Feature Acquisition Bias for Few-Shot Fine-Grained Image...

Recent work on metric-learning based few-shot fine-grained image classification (FSFGIC) has achieved promising success in classification accuracy, ...

Learning with few samples in deep learning for image classification ...

... grained granulation and calculating the similarity of relation score for few-shot classification. ... Dual class representation learning for few-shot image ...

Few-Shot Fine-Grained Image Classification via Multi-Frequency ...

To solve the challenges, we propose a novel network (FicNet) using multi-frequency neighborhood (MFN) and double-cross modulation (DCM). MFN ...