Search Results for author: Xiaoxu Li

Found 10 papers, 5 papers with code

Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification

1 code implementation30 Nov 2022 Jijie Wu, Dongliang Chang, Aneeshan Sain, Xiaoxu Li, Zhanyu Ma, Jie Cao, Jun Guo, Yi-Zhe Song

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).

Few-Shot Image Classification Few-Shot Learning +2

Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments

no code implementations17 May 2021 Xiaoxu Li, Xiaochen Yang, Zhanyu Ma, Jing-Hao Xue

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.

Classification Few-Shot Image Classification +3

TLRM: Task-level Relation Module for GNN-based Few-Shot Learning

no code implementations25 Jan 2021 Yurong Guo, Zhanyu Ma, Xiaoxu Li, Yuan Dong

We consider this method of measuring relation of samples only models the sample-to-sample relation, while neglects the specificity of different tasks.

Few-Shot Learning Relation +1

BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification

1 code implementation29 Nov 2020 Xiaoxu Li, Jijie Wu, Zhuo Sun, Zhanyu Ma, Jie Cao, Jing-Hao Xue

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.

Few-Shot Learning Fine-Grained Image Classification +1

ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification

1 code implementation27 Jun 2020 Xiaoxu Li, Liyun Yu, Xiaochen Yang, Zhanyu Ma, Jing-Hao Xue, Jie Cao, Jun Guo

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.

Classification General Classification +3

A Concise Review of Recent Few-shot Meta-learning Methods

no code implementations22 May 2020 Xiaoxu Li, Zhuo Sun, Jing-Hao Xue, Zhanyu Ma

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

1 code implementation20 Apr 2020 Xiaoxu Li, Dongliang Chang, Zhanyu Ma, Zheng-Hua Tan, Jing-Hao Xue, Jie Cao, Jingyi Yu, Jun Guo

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.

Classification General Classification

Channel Max Pooling Layer for Fine-Grained Vehicle Classification

no code implementations14 Feb 2019 Zhanyu Ma, Dongliang Chang, Xiaoxu Li

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.

Classification Fine-Grained Vehicle Classification +1

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