52 citations · 176 across the 14 of their papers we have counts for
6 papers · 1 filter
Fast Disentangled Slim Tensor Learning for Multi-view Clustering
Deng Xu, Chao Zhang, Zechao Li +2
Tensor-based multi-view clustering has recently received significant attention due to its exceptional ability to explore cross-view high-order correlations. However, most existing…
Shaping Visual Representations with Attributes for Few-Shot Recognition
Haoxing Chen, Huaxiong Li, Yaohui Li +1
Few-shot recognition aims to recognize novel categories under low-data regimes. Some recent few-shot recognition methods introduce auxiliary semantic modality, i.e., category attri…
Sparse Spatial Transformers for Few-Shot Learning
Haoxing Chen, Huaxiong Li, Yaohui Li +1
Learning from limited data is challenging because data scarcity leads to a poor generalization of the trained model. A classical global pooled representation will probably lose use…
Hierarchical Representation based Query-Specific Prototypical Network for Few-Shot Image Classification
Yaohui Li, Huaxiong Li, Haoxing Chen +1
Few-shot image classification aims at recognizing unseen categories with a small number of labeled training data. Recent metric-based frameworks tend to represent a support class b…
Multi-level Metric Learning for Few-shot Image Recognition
Haoxing Chen, Huaxiong Li, Yaohui Li +1
Few-shot learning is devoted to training a model on few samples. Most of these approaches learn a model based on a pixel-level or global-level feature representation. However, usin…
Multi-scale Adaptive Task Attention Network for Few-Shot Learning
Haoxing Chen, Huaxiong Li, Yaohui Li +1
The goal of few-shot learning is to classify unseen categories with few labeled samples. Recently, the low-level information metric-learning based methods have achieved satisfying…