Search Results for author:
Found 30 papers, 10 papers with code
UW-CVGAN: UnderWater Image Enhancement with Capsules Vectors Quantization
The degradation in the underwater images is due to wavelength-dependent light attenuation, scattering, and to the diversity of the water types in which they are captured.
Real Image Super-Resolution using GAN through modeling of LR and HR process
The current existing deep image super-resolution methods usually assume that a Low Resolution (LR) image is bicubicly downscaled of a High Resolution (HR) image.
Visual Object Tracking in First Person Vision
Despite a few previous attempts to exploit trackers in the FPV domain, a methodical analysis of the performance of state-of-the-art trackers is still missing.
Human-Object Interaction Detection
Visual Object Tracking
+1
CoCoLoT: Combining Complementary Trackers in Long-Term Visual Tracking
How to combine the complementary capabilities of an ensemble of different algorithms has been of central interest in visual object tracking.
Video-Based Reconstruction of the Trajectories Performed by Skiers
Trajectories are fundamental in different skiing disciplines.
RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural Network
Modern digital cameras and smartphones mostly rely on image signal processing (ISP) pipelines to produce realistic colored RGB images.
Resolution based Feature Distillation for Cross Resolution Person Re-Identification
Most of the existing approaches resolve the re-id task as low resolution problem in which a low resolution query image is searched in a high resolution images gallery.
Is First Person Vision Challenging for Object Tracking?
Our study extensively analyses the performance of recent visual trackers and baseline FPV trackers with respect to different aspects and considering a new performance measure.
A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution
The existing SR methods have limited performance due to a fixed degradation settings, i. e. usually a bicubic downscaling of low-resolution (LR) image.
NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
This paper reviews the NTIRE2021 challenge on burst super-resolution.
Weakly-Supervised Domain Adaptation of Deep Regression Trackers via Reinforced Knowledge Distillation
Deep regression trackers are among the fastest tracking algorithms available, and therefore suitable for real-time robotic applications.
Collaboration among Image and Object Level Features for Image Colourisation
Image colourisation is an ill-posed problem, with multiple correct solutions which depend on the context and object instances present in the input datum.
Is It a Plausible Colour? UCapsNet for Image Colourisation
Different from existing works relying on convolutional neural network models pre-trained with supervision, we cast such colourisation problem as a self-supervised learning task.
Is First Person Vision Challenging for Object Tracking?
Despite a few previous attempts to exploit trackers in FPV applications, a methodical analysis of the performance of state-of-the-art visual trackers in this domain is still missing.
Human-Object Interaction Detection
Visual Object Tracking
+1
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
Deep Iterative Residual Convolutional Network for Single Image Super-Resolution
Deep convolutional neural networks (CNNs) have recently achieved great success for single image super-resolution (SISR) task due to their powerful feature representation capabilities.
Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution
We consider a deep cyclic network structure to maintain the domain consistency between the LR and HR data distributions, which is inspired by the recent success of CycleGAN in the image-to-image translation applications.
An Exploration of Target-Conditioned Segmentation Methods for Visual Object Trackers
Visual object tracking is the problem of predicting a target object's state in a video.
Tracking-by-Trackers with a Distilled and Reinforced Model
Visual object tracking was generally tackled by reasoning independently on fast processing algorithms, accurate online adaptation methods, and fusion of trackers.
Ranked #3 on
Visual Object Tracking
on OTB-2015
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on real world super-resolution.
Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution
Most current deep learning based single image super-resolution (SISR) methods focus on designing deeper / wider models to learn the non-linear mapping between low-resolution (LR) inputs and the high-resolution (HR) outputs from a large number of paired (LR/HR) training data.
An Efficient UAV-based Artificial Intelligence Framework for Real-Time Visual Tasks
In this paper we focus on this challenge and introduce a multi-layer AI (MLAI) framework to allow easy integration of ad-hoc visual-based AI applications.
Video-Based Convolutional Attention for Person Re-Identification
In this paper we consider the problem of video-based person re-identification, which is the task of associating videos of the same person captured by different and non-overlapping cameras.
Visual Tracking by means of Deep Reinforcement Learning and an Expert Demonstrator
In the last decade many different algorithms have been proposed to track a generic object in videos.
Deep Super-Resolution Network for Single Image Super-Resolution with Realistic Degradations
Single Image Super-Resolution (SISR) aims to generate a high-resolution (HR) image of a given low-resolution (LR) image.
Aggregating Deep Pyramidal Representations for Person Re-Idenfitication
A surge of effort has been spent by the community to learn deep Re-ID models capturing a holistic single semantic level feature representation.
Ranked #16 on
Person Re-Identification
on Market-1501
Wide-Slice Residual Networks for Food Recognition
We believe that better results can be obtained if the deep architecture is defined with respect to an analysis of the food composition.
Temporal Model Adaptation for Person Re-Identification
Person re-identification is an open and challenging problem in computer vision.
Person Re-Identification Ranking Optimisation by Discriminant Context Information Analysis
In this paper, we focus on such a problem and introduce an unsupervised ranking optimization approach based on discriminant context information analysis.
