2 citations · 5 across the 8 of their papers we have counts for
7 papers · 1 filter
Few-Shot Classification with Contrastive Learning
Zhanyuan Yang, Jinghua Wang, Yingying Zhu
A two-stage training paradigm consisting of sequential pre-training and meta-training stages has been widely used in current few-shot learning (FSL) research. Many of these methods…
Dynamic Spatio-Temporal Specialization Learning for Fine-Grained Action Recognition
Tianjiao Li, Lin Geng Foo, Qiuhong Ke +4
The goal of fine-grained action recognition is to successfully discriminate between action categories with subtle differences. To tackle this, we derive inspiration from the human…
SA-Net: A deep spectral analysis network for image clustering
Jinghua Wang, Jianmin Jiang
Although supervised deep representation learning has attracted enormous attentions across areas of pattern recognition and computer vision, little progress has been made towards un…
Spectral Analysis Network for Deep Representation Learning and Image Clustering
Jinghua Wang, Adrian Hilton, Jianmin Jiang
Deep representation learning is a crucial procedure in multimedia analysis and attracts increasing attention. Most of the popular techniques rely on convolutional neural network an…
An unsupervised deep learning framework via integrated optimization of representation learning and GMM-based modeling
Jinghua Wang, Jianmin Jiang
While supervised deep learning has achieved great success in a range of applications, relatively little work has studied the discovery of knowledge from unlabeled data. In this pap…
Conditional Coupled Generative Adversarial Networks for Zero-Shot Domain Adaptation
Jinghua Wang, Jianmin Jiang
Machine learning models trained in one domain perform poorly in the other domains due to the existence of domain shift. Domain adaptation techniques solve this problem by training…
Adversarial Learning for Zero-shot Domain Adaptation
Jinghua Wang, Jianmin Jiang
Zero-shot domain adaptation (ZSDA) is a category of domain adaptation problems where neither data sample nor label is available for parameter learning in the target domain. With th…