10 citations · 25 across the 6 of their papers we have counts for
6 papers
When Few-Shot Learning Meets Video Object Detection
Zhongjie Yu, Gaoang Wang, Lin Chen +2
Different from static images, videos contain additional temporal and spatial information for better object detection. However, it is costly to obtain a large number of videos with…
DAIL: Dataset-Aware and Invariant Learning for Face Recognition
Gaoang Wang, Lin Chen, Tianqiang Liu +2
To achieve good performance in face recognition, a large scale training dataset is usually required. A simple yet effective way to improve recognition performance is to use a datas…
Region Comparison Network for Interpretable Few-shot Image Classification
Zhiyu Xue, Lixin Duan, Wen Li +2
While deep learning has been successfully applied to many real-world computer vision tasks, training robust classifiers usually requires a large amount of well-labeled data. Howeve…
TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning
Zhongjie Yu, Lin Chen, Zhongwei Cheng +1
The successful application of deep learning to many visual recognition tasks relies heavily on the availability of a large amount of labeled data which is usually expensive to obta…
Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation
Yiming Xu, Lin Chen, Zhongwei Cheng +2
We study the problem of visual question answering (VQA) in images by exploiting supervised domain adaptation, where there is a large amount of labeled data in the source domain but…
Known-class Aware Self-ensemble for Open Set Domain Adaptation
Qing Lian, Wen Li, Lin Chen +1
Existing domain adaptation methods generally assume different domains have the identical label space, which is quite restrict for real-world applications. In this paper, we focus o…