45 citations · 108 across the 6 of their papers we have counts for
6 papers
Progressive Representative Labeling for Deep Semi-Supervised Learning
Xiaopeng Yan, Riquan Chen, Litong Feng +3
Deep semi-supervised learning (SSL) has experienced significant attention in recent years, to leverage a huge amount of unlabeled data to improve the performance of deep learning w…
Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition
Tianshui Chen, Liang Lin, Riquan Chen +2
Recognizing multiple labels of an image is a practical yet challenging task, and remarkable progress has been achieved by searching for semantic regions and exploiting label depend…
Knowledge Graph Transfer Network for Few-Shot Recognition
Riquan Chen, Tianshui Chen, Xiaolu Hui +3
Few-shot learning aims to learn novel categories from very few samples given some base categories with sufficient training samples. The main challenge of this task is the novel cat…
Knowledge-Embedded Routing Network for Scene Graph Generation
Tianshui Chen, Weihao Yu, Riquan Chen +1
To understand a scene in depth not only involves locating/recognizing individual objects, but also requires to infer the relationships and interactions among them. However, since t…
Neural Task Planning with And-Or Graph Representations
Tianshui Chen, Riquan Chen, Lin Nie +3
This paper focuses on semantic task planning, i.e., predicting a sequence of actions toward accomplishing a specific task under a certain scene, which is a new problem in computer…
Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition
Tianshui Chen, Liang Lin, Riquan Chen +2
Humans can naturally understand an image in depth with the aid of rich knowledge accumulated from daily lives or professions. For example, to achieve fine-grained image recognition…