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Found 6 papers, 5 papers with code
The Treasure Beneath Multiple Annotations: An Uncertainty-aware Edge Detector
Deep learning-based edge detectors heavily rely on pixel-wise labels which are often provided by multiple annotators.
EDTER: Edge Detection with Transformer
In Stage I, a global transformer encoder is used to capture long-range global context on coarse-grained image patches.
RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth
Taking into consideration the distinct attributes of each type of edges and the relationship between them, RINDNet learns effective representations for each of them and works in three stages.
Learning from Pixel-Level Label Noise: A New Perspective for Semi-Supervised Semantic Segmentation
In particular, for the generated pixel-level noisy labels from weak supervisions by Class Activation Map (CAM), we train a clean segmentation model with strong supervisions to detect the clean labels from these noisy labels according to the cross-entropy loss.
Shadow Removal by a Lightness-Guided Network with Training on Unpaired Data
In this paper, we present a new Lightness-Guided Shadow Removal Network (LG-ShadowNet) for shadow removal by training on unpaired data.
Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features
To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs).
