Search Results for author:
Found 9 papers, 5 papers with code
Exemplar-free Continual Learning of Vision Transformers via Gated Class-Attention and Cascaded Feature Drift Compensation
Secondly, we propose a new method of feature drift compensation that accommodates feature drift in the backbone when learning new tasks.
Explaining Image Enhancement Black-Box Methods through a Path Planning Based Algorithm
In this paper we present a path planning algorithm which provides a step-by-step explanation of the output produced by state of the art enhancement methods, overcoming black-box limitation.
Offset equivariant networks and their applications
In this paper we present a framework for the design and implementation of offset equivariant networks, that is, neural networks that preserve in their output uniform increments in the input.
TreEnhance: A Tree Search Method For Low-Light Image Enhancement
Given as input a low-light image, TreEnhance produces as output its enhanced version together with the sequence of image editing operations used to obtain it.
Ranked #1 on
Image Enhancement
on MIT-Adobe FiveK
Single and Multiple Illuminant Estimation Using Convolutional Neural Networks
In this paper we present a method for the estimation of the color of the illuminant in RAW images.
Evaluating color texture descriptors under large variations of controlled lighting conditions
The recognition of color texture under varying lighting conditions is still an open issue.
CURL: Co-trained Unsupervised Representation Learning for Image Classification
The strategy, that is called CURL from Co-trained Unsupervised Representation Learning, iteratively builds two classifiers on two different views of the data.
Color Constancy Using CNNs
In this work we describe a Convolutional Neural Network (CNN) to accurately predict the scene illumination.
Remote sensing image classification exploiting multiple kernel learning
Experimental results on publicly available datasets demonstrate the feasibility of the proposed approach.
