415 citations · 707 across the 10 of their papers we have counts for
10 papers
Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification
Jijie Wu, Dongliang Chang, Aneeshan Sain +5
The main challenge for fine-grained few-shot image classification is to learn feature representations with higher inter-class and lower intra-class variations, with a mere few labe…
Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments
Xiaoxu Li, Xiaochen Yang, Zhanyu Ma +1
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. One main solution to few…
TLRM: Task-level Relation Module for GNN-based Few-Shot Learning
Yurong Guo, Zhanyu Ma, Xiaoxu Li +1
Recently, graph neural networks (GNNs) have shown powerful ability to handle few-shot classification problem, which aims at classifying unseen samples when trained with limited lab…
BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification
Xiaoxu Li, Jijie Wu, Zhuo Sun +3
Few-shot learning for fine-grained image classification has gained recent attention in computer vision. Among the approaches for few-shot learning, due to the simplicity and effect…
CC-Loss: Channel Correlation Loss For Image Classification
Zeyu Song, Dongliang Chang, Zhanyu Ma +2
The loss function is a key component in deep learning models. A commonly used loss function for classification is the cross entropy loss, which is a simple yet effective applicatio…
ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification
Xiaoxu Li, Liyun Yu, Xiaochen Yang +4
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 samp…
A Concise Review of Recent Few-shot Meta-learning Methods
Xiaoxu Li, Zhuo Sun, Jing-Hao Xue +1
Few-shot meta-learning has been recently reviving with expectations to mimic humanity's fast adaption to new concepts based on prior knowledge. In this short communication, we give…
OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer
Xiaoxu Li, Dongliang Chang, Zhanyu Ma +5
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. To mitigate overfitting…
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
Dongliang Chang, Yifeng Ding, Jiyang Xie +6
Key for solving fine-grained image categorization is finding discriminate and local regions that correspond to subtle visual traits. Great strides have been made, with complex netw…
Channel Max Pooling Layer for Fine-Grained Vehicle Classification
Zhanyu Ma, Dongliang Chang, Xiaoxu Li
Deep convolutional networks have recently shown excellent performance on Fine-Grained Vehicle Classification. Based on these existing works, we consider that the back-probation alg…