Search Results for author:
Found 7 papers, 2 papers with code
Rain rendering for evaluating and improving robustness to bad weather
In this context, we present a rain rendering pipeline that enables the systematic evaluation of common computer vision algorithms to controlled amounts of rain.
Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution
In this paper, we show that the root cause of these pattern artifacts can be traced back to a mismatch between the pre-training objective of perceptual loss and the super-resolution objective.
MA 3 : Model Agnostic Adversarial Augmentation for Few Shot learning
Despite the recent developments in vision-related problems using deep neural networks, there still remains a wide scope in the improvement of generalizing these models to unseen examples.
Physics-Based Rendering for Improving Robustness to Rain
Our rendering relies on a physical particle simulator, an estimation of the scene lighting and an accurate rain photometric modeling to augment images with arbitrary amount of realistic rain or fog.
Perceptual Conditional Generative Adversarial Networks for End-to-End Image Colourization
Colours are everywhere.
Movie Recommendation System using Sentiment Analysis from Microblogging Data
Traditional approaches in recommendation systems include collaborative filtering and content-based filtering.
Reconstruction Loss Minimized FCN for Single Image Dehazing
In this paper, we propose a Fully Convolutional Neural Network based model to recover the clear scene radiance by estimating the environmental illumination and the scene transmittance jointly from a hazy image.
