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Found 6 papers, 2 papers with code
MetaSeg: Content-Aware Meta-Net for Omni-Supervised Semantic Segmentation
Noisy labels, inevitably existing in pseudo segmentation labels generated from weak object-level annotations, severely hampers model optimization for semantic segmentation.
Automated Optical Inspection of FAST's Reflector Surface using Drones and Computer Vision
Hence, the timely and accurate detection of surface defects is crucial for FAST's stable operation.
Dynamic Loss For Robust Learning
Label noise and class imbalance commonly coexist in real-world data.
Ranked #6 on
Learning with noisy labels
on ANIMAL
Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Data
In practice, however, biased samples with corrupted labels and of tailed classes commonly co-exist in training data.
Pyramid Correlation based Deep Hough Voting for Visual Object Tracking
Through experiments we find that, without regression, the performance could be equally promising as long as we delicately design the network to suit the training objective.
Stochastic Channel Decorrelation Network and Its Application to Visual Tracking
Deep convolutional neural networks (CNNs) have dominated many computer vision domains because of their great power to extract good features automatically.
