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Found 24 papers, 5 papers with code
Pathspace Kalman Filters with Dynamic Process Uncertainty for Analyzing Time-course Data
Here, we develop an extension of the KF, called a Pathspace Kalman Filter (PKF) which allows us to a) dynamically track the uncertainties associated with the underlying data and prior knowledge, and b) take as input an entire trajectory and an underlying mechanistic model, and using a Bayesian methodology quantify the different sources of uncertainty.
Improving Image Captioning Descriptiveness by Ranking and LLM-based Fusion
This advance opens up new possibilities for generating captions that are more suitable for the training of both vision-language and captioning models.
ENRICH: Multi-purposE dataset for beNchmaRking In Computer vision and pHotogrammetry
The availability of high-resolution data and accurate ground truth is essential to evaluate and compare methods and algorithms properly.
AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
The homepage of this challenge is at https://github. com/RenYang-home/AIM22_CompressSR.
Semi-supervised cross-lingual speech emotion recognition
Performance in Speech Emotion Recognition (SER) on a single language has increased greatly in the last few years thanks to the use of deep learning techniques.
Shallow camera pipeline for night photography rendering
We introduce a camera pipeline for rendering visually pleasing photographs in low light conditions, as part of the NTIRE2022 Night Photography Rendering challenge.
Adaptive Epidemic Forecasting and Community Risk Evaluation of COVID-19
Pandemic control measures like lock-down, restrictions on restaurants and gatherings, social-distancing have shown to be effective in curtailing the spread of COVID-19.
Illumination Estimation Challenge: experience of past two years
The main advantage of testing a method on a challenge over testing in on some of the known datasets is the fact that the ground-truth illuminations for the challenge test images are unknown up until the results have been submitted, which prevents any potential hyperparameter tuning that may be biased.
Disentangling Image Distortions in Deep Feature Space
In this work we take a further step in the direction of a broader understanding of such property by analyzing the capability of deep visual representations to intrinsically characterize different types of image distortions.
Quasi-Unsupervised Color Constancy
After training, unbalanced images can be processed thanks to the preliminary conversion to grayscale of the input to the neural network.
Deep Residual Autoencoder for quality independent JPEG restoration
This results is remarkable since the approaches in the state of the art use a different set of weights for each compression quality, while the proposed model uses the same weights for all of them, making it applicable to images in the wild where the QF used for compression is unkwnown.
Multitask Painting Categorization by Deep Multibranch Neural Network
In this work we propose a new deep multibranch neural network to solve the tasks of artist, style, and genre categorization in a multitask formulation.
Benchmark Analysis of Representative Deep Neural Network Architectures
This work presents an in-depth analysis of the majority of the deep neural networks (DNNs) proposed in the state of the art for image recognition.
Rehabilitating the ColorChecker Dataset for Illuminant Estimation
In a previous work, it was shown that there is a curious problem with the benchmark ColorChecker dataset for illuminant estimation.
Aesthetics Assessment of Images Containing Faces
Recent research has widely explored the problem of aesthetics assessment of images with generic content.
Automated Pruning for Deep Neural Network Compression
In this work we present a method to improve the pruning step of the current state-of-the-art methodology to compress neural networks.
Deep Learning for Logo Recognition
In this paper we propose a method for logo recognition using deep learning.
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Large age-gap face verification by feature injection in deep networks
This paper introduces a new method for face verification across large age gaps and also a dataset containing variations of age in the wild, the Large Age-Gap (LAG) dataset, with images ranging from child/young to adult/old.
On the Use of Deep Learning for Blind Image Quality Assessment
We report on different design choices, ranging from the use of features extracted from pre-trained Convolutional Neural Networks (CNNs) as a generic image description, to the use of features extracted from a CNN fine-tuned for the image quality task.
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.
User Preferences Modeling and Learning for Pleasing Photo Collage Generation
The relative importance of these criteria are automatically learned by exploiting the user preferences, and new collages are generated.
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.
How Far Can You Get By Combining Change Detection Algorithms?
Given the existence of many change detection algorithms, each with its own peculiarities and strengths, we propose a combination strategy, that we termed IUTIS (In Unity There Is Strength), based on a genetic Programming framework.
Color Constancy Using CNNs
In this work we describe a Convolutional Neural Network (CNN) to accurately predict the scene illumination.
