48 citations · 78 across the 6 of their papers we have counts for
6 papers · 1 filter
Generalized Few-shot Semantic Segmentation
Zhuotao Tian, Xin Lai, Li Jiang +4
Training semantic segmentation models requires a large amount of finely annotated data, making it hard to quickly adapt to novel classes not satisfying this condition. Few-Shot Seg…
Prior Guided Feature Enrichment Network for Few-Shot Segmentation
Zhuotao Tian, Hengshuang Zhao, Michelle Shu +3
State-of-the-art semantic segmentation methods require sufficient labeled data to achieve good results and hardly work on unseen classes without fine-tuning. Few-shot segmentation…
Understanding Human Hands in Contact at Internet Scale
Dandan Shan, Jiaqi Geng, Michelle Shu +1
Hands are the central means by which humans manipulate their world and being able to reliably extract hand state information from Internet videos of humans engaged in their hands h…
Identifying Model Weakness with Adversarial Examiner
Michelle Shu, Chenxi Liu, Weichao Qiu +1
Machine learning models are usually evaluated according to the average case performance on the test set. However, this is not always ideal, because in some sensitive domains (e.g.…
Region Refinement Network for Salient Object Detection
Zhuotao Tian, Hengshuang Zhao, Michelle Shu +4
Albeit intensively studied, false prediction and unclear boundaries are still major issues of salient object detection. In this paper, we propose a Region Refinement Network (RRN),…
Facelet-Bank for Fast Portrait Manipulation
Ying-Cong Chen, Huaijia Lin, Michelle Shu +5
Digital face manipulation has become a popular and fascinating way to touch images with the prevalence of smartphones and social networks. With a wide variety of user preferences,…