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Found 13 papers, 4 papers with code
Deepfake Detection: A Comprehensive Study from the Reliability Perspective
In this survey, we provide a thorough review of the existing Deepfake detection studies from the reliability perspective.
Removing Rain Streaks via Task Transfer Learning
Inspired by our studies, we propose to remove rain by learning favorable deraining representations from other connected tasks.
Ghost-free High Dynamic Range Imaging with Context-aware Transformer
High dynamic range (HDR) deghosting algorithms aim to generate ghost-free HDR images with realistic details.
Multi-Decoding Deraining Network and Quasi-Sparsity Based Training
In this work, we aim to exploit the intrinsic priors of rainy images and develop intrinsic loss functions to facilitate training deraining networks, which decompose a rainy image into a rain-free background layer and a rainy layer containing intact rain streaks.
Rethinking Image Deraining via Rain Streaks and Vapors
Single image deraining regards an input image as a fusion of a background image, a transmission map, rain streaks, and atmosphere light.
Deep Image Deraining Via Intrinsic Rainy Image Priors and Multi-scale Auxiliary Decoding
Different rain models and novel network structures have been proposed to remove rain streaks from single rainy images.
Gradient Information Guided Deraining with A Novel Network and Adversarial Training
However, the existing methods usually do not have good generalization ability, which leads to the fact that almost all of existing methods have a satisfied performance on removing a specific type of rain streaks, but may have a relatively poor performance on other types of rain streaks.
User Diverse Preference Modeling by Multimodal Attentive Metric Learning
To tackle this problem, in this paper, we propose a novel Multimodal Attentive Metric Learning (MAML) method to model user diverse preferences for various items.
Deep Single Image Deraining Via Estimating Transmission and Atmospheric Light in rainy Scenes
Instead of using the estimated atmospheric light directly to learn a network to calculate transmission, we utilize it as ground truth and design a simple but novel triangle-shaped network structure to learn atmospheric light for every rainy image, then fine-tune the network to obtain a better estimation of atmospheric light during the training of transmission network.
An Effective Two-Branch Model-Based Deep Network for Single Image Deraining
Removing rain effects from an image is of importance for various applications such as autonomous driving, drone piloting, and photo editing.
Quantifying and Alleviating the Language Prior Problem in Visual Question Answering
Benefiting from the advancement of computer vision, natural language processing and information retrieval techniques, visual question answering (VQA), which aims to answer questions about an image or a video, has received lots of attentions over the past few years.
Rain Removal By Image Quasi-Sparsity Priors
We present a novel rain removal method in this paper, which consists of two steps, i. e., detection of rain streaks and reconstruction of the rain-removed image.
Removing rain streaks by a linear model
Removing rain streaks from a single image continues to draw attentions today in outdoor vision systems.
