2 citations · 3 across the 6 of their papers we have counts for
6 papers
MetaSeg: Content-Aware Meta-Net for Omni-Supervised Semantic Segmentation
Shenwang Jiang, Jianan Li, Ying Wang +4
Noisy labels, inevitably existing in pseudo segmentation labels generated from weak object-level annotations, severely hampers model optimization for semantic segmentation. Previou…
Automated Optical Inspection of FAST's Reflector Surface using Drones and Computer Vision
Jianan Li, Shenwang Jiang, Liqiang Song +5
The Five-hundred-meter Aperture Spherical radio Telescope (FAST) is the world's largest single-dish radio telescope. Its large reflecting surface achieves unprecedented sensitivity…
Dynamic Loss For Robust Learning
Shenwang Jiang, Jianan Li, Jizhou Zhang +2
Label noise and class imbalance commonly coexist in real-world data. Previous works for robust learning, however, usually address either one type of the data biases and underperfor…
Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Data
Shenwang Jiang, Jianan Li, Ying Wang +3
Corrupted labels and class imbalance are commonly encountered in practically collected training data, which easily leads to over-fitting of deep neural networks (DNNs). Existing ap…
Pyramid Correlation based Deep Hough Voting for Visual Object Tracking
Ying Wang, Tingfa Xu, Jianan Li +2
Most of the existing Siamese-based trackers treat tracking problem as a parallel task of classification and regression. However, some studies show that the sibling head structure c…
Stochastic Channel Decorrelation Network and Its Application to Visual Tracking
Jie Guo, Tingfa Xu, Shenwang Jiang +1
Deep convolutional neural networks (CNNs) have dominated many computer vision domains because of their great power to extract good features automatically. However, many deep CNNs-b…