Search Results for author:
Found 5 papers, 4 papers with code
Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution
Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow.
ELDA: Using Edges to Have an Edge on Semantic Segmentation Based UDA
Despite their effectiveness, using depth as domain invariant information in UDA tasks may lead to multiple issues, such as excessively high extraction costs and difficulties in achieving a reliable prediction quality.
Denoising Likelihood Score Matching for Conditional Score-based Data Generation
These methods facilitate the training procedure of conditional score models, as a mixture of scores can be separately estimated using a score model and a classifier.
CLCC: Contrastive Learning for Color Constancy
In this paper, we present CLCC, a novel contrastive learning framework for color constancy.
One-Shot Object Detection with Co-Attention and Co-Excitation
This paper aims to tackle the challenging problem of one-shot object detection.
Ranked #2 on
One-Shot Object Detection
on COCO
