Search Results for author:
Found 3 papers, 1 papers with code
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.
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).
Unsupervised Part Mining for Fine-grained Image Classification
To tackle this issue, we propose a fully unsupervised part mining (UPM) approach to localize the discriminative parts without even image-level annotations, which largely improves the fine-grained classification performance.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
