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Found 9 papers, 7 papers with code
TPSeNCE: Towards Artifact-Free Realistic Rain Generation for Deraining and Object Detection in Rain
We first introduce a Triangular Probability Similarity (TPS) constraint to guide the generated images toward clear and rainy images in the discriminator manifold, thereby minimizing artifacts and distortions during rain generation.
Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond
This paper presents a comprehensive survey of low-light image and video enhancement, addressing two primary challenges in the field.
A Survey on Causal Representation Learning and Future Work for Medical Image Analysis
Statistical machine learning algorithms have achieved state-of-the-art results on benchmark datasets, outperforming humans in many tasks.
PointNorm: Dual Normalization is All You Need for Point Cloud Analysis
Point cloud analysis is challenging due to the irregularity of the point cloud data structure.
AS-IntroVAE: Adversarial Similarity Distance Makes Robust IntroVAE
However, due to the unavailability of an effective metric to evaluate the difference between the real and the fake images, the posterior collapse and the vanishing gradient problem still exist, reducing the fidelity of the synthesized images.
Functional Optimization Reinforcement Learning for Real-Time Bidding
In order to evaluate the proposed RTB strategy's performance, we demonstrate the results on ten sequential simulated auction campaigns.
Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization
This paper introduces UDA-VAE++, an unsupervised domain adaptation framework for cardiac segmentation with a compact loss function lower bound.
Asian Giant Hornet Control based on Image Processing and Biological Dispersal
Fourth, we apply ordinary differential equations to examine AGH numbers at the different natural growthrate and reaction speed and output the potential propagation coefficient.
SAPNet: Segmentation-Aware Progressive Network for Perceptual Contrastive Deraining
To address this issue, in this paper, we present a segmentation-aware progressive network (SAPNet) based upon contrastive learning for single image deraining.
