52 citations · 176 across the 14 of their papers we have counts for
5 papers · 1 filter
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…
Perspective-corrected Spatial Referring Expression Generation for Human-Robot Interaction
Mingjiang Liu, Chengli Xiao, Chunlin Chen
Intelligent robots designed to interact with humans in real scenarios need to be able to refer to entities actively by natural language. In spatial referring expression generation,…
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…