44 citations · 191 across the 34 of their papers we have counts for
6 papers · 1 filter
Delving into Probabilistic Uncertainty for Unsupervised Domain Adaptive Person Re-Identification
Jian Han, Ya-Li li, Shengjin Wang
Clustering-based unsupervised domain adaptive (UDA) person re-identification (ReID) reduces exhaustive annotations. However, owing to unsatisfactory feature embedding and imperfect…
Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification
Zhenyu Wang, Yali Li, Ye Guo +1
Semi-supervised learning aims to leverage a large amount of unlabeled data for performance boosting. Existing works primarily focus on image classification. In this paper, we delve…
Do Different Tracking Tasks Require Different Appearance Models?
Zhongdao Wang, Hengshuang Zhao, Ya-Li Li +3
Tracking objects of interest in a video is one of the most popular and widely applicable problems in computer vision. However, with the years, a Cambrian explosion of use cases and…
AdaZoom: Adaptive Zoom Network for Multi-Scale Object Detection in Large Scenes
Jingtao Xu, Yali Li, Shengjin Wang
Detection in large-scale scenes is a challenging problem due to small objects and extreme scale variation. It is essential to focus on the image regions of small objects. In this p…
A^2-FPN: Attention Aggregation based Feature Pyramid Network for Instance Segmentation
Miao Hu, Yali Li, Lu Fang +1
Learning pyramidal feature representations is crucial for recognizing object instances at different scales. Feature Pyramid Network (FPN) is the classic architecture to build a fea…
Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised Object Detection
Zhenyu Wang, Yali Li, Ye Guo +2
In this paper, we delve into semi-supervised object detection where unlabeled images are leveraged to break through the upper bound of fully-supervised object detection models. Pre…