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Found 19 papers, 5 papers with code
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
Tracking Skiers from the Top to the Bottom
To enable the study, the largest and most annotated dataset for computer vision in skiing, SkiTB, is introduced.
UW-ProCCaps: UnderWater Progressive Colourisation with Capsules
The progressive training gives the ground for a generative adversarial routine focused on the refining of colours giving the image bright and saturated colours which bring the image back to life.
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.
TUCaN: Progressively Teaching Colourisation to Capsules
We performed a user study to quantify the perceptual realism of the colourisation results demonstrating: that progressive learning let the TUCaN achieve better colours than the end to end scheme; and pointing out the limitations of the existing evaluation metrics.
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.
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 #5 on
Visual Object Tracking
on OTB-2015
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.
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 #18 on
Person Re-Identification
on Market-1501
Group Re-Identification via Unsupervised Transfer of Sparse Features Encoding
First, a dictionary of sparse atoms is learned using patches extracted from single person images.
The UMCD Dataset
In recent years, the technological improvements of low-cost small-scale Unmanned Aerial Vehicles (UAVs) are promoting an ever-increasing use of them in different tasks.
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.
