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Found 17 papers, 8 papers with code
UCL-Dehaze: Towards Real-world Image Dehazing via Unsupervised Contrastive Learning
From a different yet new perspective, this paper explores contrastive learning with an adversarial training effort to leverage unpaired real-world hazy and clean images, thus bridging the gap between synthetic and real-world haze is avoided.
TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning
Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in these scenarios.
Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for Mixture of Rain Removal
Rain is one of the most common weather which can completely degrade the image quality and interfere with the performance of many computer vision tasks, especially under heavy rain conditions.
Neural Related Work Summarization with a Joint Context-driven Attention Mechanism
Conventional solutions to automatic related work summarization rely heavily on human-engineered features.
Contrastive Semantic-Guided Image Smoothing Network
Image smoothing is a fundamental low-level vision task that aims to preserve salient structures of an image while removing insignificant details.
Detail-recovery Image Deraining via Dual Sample-augmented Contrastive Learning
To bridge the two domain gaps, we propose a semi-supervised detail-recovery image deraining network (Semi-DRDNet) with dual sample-augmented contrastive learning.
Joint Depth Estimation and Mixture of Rain Removal From a Single Image
In this study, we propose an effective image deraining paradigm for Mixture of rain REmoval, called DEMore-Net, which takes full account of the MOR effect.
FriendNet: Detection-Friendly Dehazing Network
In this paper, we raise an intriguing question: can the combination of image restoration and object detection enhance detection performance in adverse weather conditions?
Community-Based Data Integration of Course and Job Data in Support of Personalized Career-Education Recommendations
How does your education impact your professional career?
Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases
Question generation over knowledge bases (KBQG) aims at generating natural-language questions about a subgraph, i. e. a set of (connected) triples.
Knowledge-aware Method for Confusing Charge Prediction
In this paper, we propose a knowledge-attentive neural network model, which introduces legal schematic knowledge about charges and exploit the knowledge hierarchical representation as the discriminative features to differentiate confusing charges.
Leveraging Online Shopping Behaviors as a Proxy for Personal Lifestyle Choices: New Insights into Chronic Disease Prevention Literacy
Objective: Ubiquitous internet access is reshaping the way we live, but it is accompanied by unprecedented challenges in preventing chronic diseases that are usually planted by long exposure to unhealthy lifestyles.
Semi-UFormer: Semi-supervised Uncertainty-aware Transformer for Image Dehazing
Semi-UFormer can well leverage both the real-world hazy images and their uncertainty guidance information.
iSmallNet: Densely Nested Network with Label Decoupling for Infrared Small Target Detection
First, to maintain small targets in deep layers, we develop a multi-scale nested interaction module to explore a wide range of context information.
RainDiffusion: When Unsupervised Learning Meets Diffusion Models for Real-world Image Deraining
Before answering it, we observe two major obstacles of diffusion models in real-world image deraining: the need for paired training data and the limited utilization of multi-scale rain patterns.
Uncertainty-Driven Multi-Scale Feature Fusion Network for Real-time Image Deraining
Therefore, we propose an Uncertainty-Driven Multi-Scale Feature Fusion Network (UMFFNet) that learns the probability mapping distribution between paired images to estimate uncertainty.
Knowledge Pyramid: A Novel Hierarchical Reasoning Structure for Generalized Knowledge Augmentation and Inference
Knowledge graph (KG) based reasoning has been regarded as an effective means for the analysis of semantic networks and is of great usefulness in areas of information retrieval, recommendation, decision-making, and man-machine interaction.
