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Found 8 papers, 1 papers with code
Zero-Shot Image Super-Resolution with Depth Guided Internal Degradation Learning
In the past few years, we have witnessed the great progress of image super-resolution (SR) thanks to the power of deep learning.
Learning the Redundancy-free Features for Generalized Zero-Shot Object Recognition
Zero-shot object recognition or zero-shot learning aims to transfer the object recognition ability among the semantically related categories, such as fine-grained animal or bird species.
NTIRE 2020 Challenge on Image Demoireing: Methods and Results
Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image.
AIM 2019 Challenge on Image Demoireing: Methods and Results
A new dataset, called LCDMoire was created for this challenge, and consists of 10, 200 synthetically generated image pairs (moire and clean ground truth).
Multi-scale Dynamic Feature Encoding Network for Image Demoireing
The prevalence of digital sensors, such as digital cameras and mobile phones, simplifies the acquisition of photos.
Unsupervised Domain Adaptation for Zero-Shot Learning
Zero-shot learning (ZSL) can be considered as a special case of transfer learning where the source and target domains have different tasks/label spaces and the target domain is unlabelled, providing little guidance for the knowledge transfer.
Semantic Graph for Zero-Shot Learning
Specifically, in contrast to previous work which ignores the semantic relationships between seen classes and focus merely on those between seen and unseen classes, in this paper a novel approach based on a semantic graph is proposed to represent the relationships between all the seen and unseen class in a semantic word space.
Can Image-Level Labels Replace Pixel-Level Labels for Image Parsing
By oversegmenting all the images into regions, we formulate noisily tagged image parsing as a weakly supervised sparse learning problem over all the regions, where the initial labels of each region are inferred from image-level labels.
