111 citations · 131 across the 4 of their papers we have counts for
4 papers
SASFormer: Transformers for Sparsely Annotated Semantic Segmentation
Hui Su, Yue Ye, Wei Hua +2
Semantic segmentation based on sparse annotation has advanced in recent years. It labels only part of each object in the image, leaving the remainder unlabeled. Most of the existin…
Re-Attention Transformer for Weakly Supervised Object Localization
Hui Su, Yue Ye, Zhiwei Chen +2
Weakly supervised object localization is a challenging task which aims to localize objects with coarse annotations such as image categories. Existing deep network approaches are ma…
Deep Structured Prediction for Facial Landmark Detection
Lisha Chen, Hui Su, Qiang Ji
Existing deep learning based facial landmark detection methods have achieved excellent performance. These methods, however, do not explicitly embed the structural dependencies amon…
Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems
Jaimie Drozdal, Justin Weisz, Dakuo Wang +6
We explore trust in a relatively new area of data science: Automated Machine Learning (AutoML). In AutoML, AI methods are used to generate and optimize machine learning models by a…