272 citations · 690 across the 13 of their papers we have counts for
15 papers
RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features
Gang Zhang, Xin Lu, Jingru Tan +4
The two-stage methods for instance segmentation, e.g. Mask R-CNN, have achieved excellent performance recently. However, the segmented masks are still very coarse due to the downsa…
Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation
Chufeng Tang, Hang Chen, Xiao Li +3
Tremendous efforts have been made on instance segmentation but the mask quality is still not satisfactory. The boundaries of predicted instance masks are usually imprecise due to t…
CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds
Ge Gao, Mikko Lauri, Xiaolin Hu +2
It is often desired to train 6D pose estimation systems on synthetic data because manual annotation is expensive. However, due to the large domain gap between the synthetic and rea…
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
Xiang Li, Wenhai Wang, Lijun Wu +5
One-stage detector basically formulates object detection as dense classification and localization. The classification is usually optimized by Focal Loss and the box location is com…
End-to-End Face Parsing via Interlinked Convolutional Neural Networks
Zi Yin, Valentin Yiu, Xiaolin Hu +1
Face parsing is an important computer vision task that requires accurate pixel segmentation of facial parts (such as eyes, nose, mouth, etc.), providing a basis for further face an…
6D Object Pose Regression via Supervised Learning on Point Clouds
Ge Gao, Mikko Lauri, Yulong Wang +3
This paper addresses the task of estimating the 6 degrees of freedom pose of a known 3D object from depth information represented by a point cloud. Deep features learned by convolu…