40 citations · 95 across the 8 of their papers we have counts for
8 papers
Model Calibration in Dense Classification with Adaptive Label Perturbation
Jiawei Liu, Changkun Ye, Shan Wang +4
For safety-related applications, it is crucial to produce trustworthy deep neural networks whose prediction is associated with confidence that can represent the likelihood of corre…
P2C: Self-Supervised Point Cloud Completion from Single Partial Clouds
Ruikai Cui, Shi Qiu, Saeed Anwar +4
Point cloud completion aims to recover the complete shape based on a partial observation. Existing methods require either complete point clouds or multiple partial observations of…
Generalized Few-Shot 3D Object Detection of LiDAR Point Cloud for Autonomous Driving
Jiawei Liu, Xingping Dong, Sanyuan Zhao +1
Recent years have witnessed huge successes in 3D object detection to recognize common objects for autonomous driving (e.g., vehicles and pedestrians). However, most methods rely he…
Generalised Co-Salient Object Detection
Jiawei Liu, Jing Zhang, Ruikai Cui +3
We propose a new setting that relaxes an assumption in the conventional Co-Salient Object Detection (CoSOD) setting by allowing the presence of "noisy images" which do not show the…
Semi-supervised Salient Object Detection with Effective Confidence Estimation
Jiawei Liu, Jing Zhang, Nick Barnes
The success of existing salient object detection models relies on a large pixel-wise labeled training dataset, which is time-consuming and expensive to obtain. We study semi-superv…
Confidence-Aware Learning for Camouflaged Object Detection
Jiawei Liu, Jing Zhang, Nick Barnes
Confidence-aware learning is proven as an effective solution to prevent networks becoming overconfident. We present a confidence-aware camouflaged object detection framework using…
Learning structure-aware semantic segmentation with image-level supervision
Jiawei Liu, Jing Zhang, Yicong Hong +1
Compared with expensive pixel-wise annotations, image-level labels make it possible to learn semantic segmentation in a weakly-supervised manner. Within this pipeline, the class ac…
Local Label Point Correction for Edge Detection of Overlapping Cervical Cells
Jiawei Liu, Huijie Fan, Qiang Wang +5
Accurate labeling is essential for supervised deep learning methods. However, it is almost impossible to accurately and manually annotate thousands of images, which results in many…