Outputs (358)

Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection (2019)
Presentation / Conference Contribution

Despite inherent ill-definition, anomaly detection is a research endeavour of great interest within machine learning and visual scene understanding alike. Most commonly, anomaly detection is considered as the detection of outliers within a given data... Read More about Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection.

Cutting-Edge VR/AR Display Technologies (Gaze-, Accommodation-, Motion-aware and HDR-enabled) (2018)
Presentation / Conference Contribution

Near-eye (VR/AR) displays suffer from technical, interaction as well as visual quality issues which hinder their commercial potential. This tutorial will deliver an overview of cutting-edge VR/AR display technologies, focusing on technical, interacti... Read More about Cutting-Edge VR/AR Display Technologies (Gaze-, Accommodation-, Motion-aware and HDR-enabled).

Modeling Detailed Cloud Scene from Multi-source Images (2018)
Presentation / Conference Contribution

Realistic cloud is essential for enhancing the quality of computer graphics applications, such as flight simulation. Data-driven method is an effective way in cloud modeling, but existing methods typically only utilize one data source as input. For e... Read More about Modeling Detailed Cloud Scene from Multi-source Images.

Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition (2019)
Presentation / Conference Contribution

Unsupervised domain adaptation aims to transfer knowledge from a source domain to a target domain so that the target domain data can be recognized without any explicit labelling information for this domain. One limitation of the problem setting is th... Read More about Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition.

Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation (2019)
Presentation / Conference Contribution

This paper addresses the challenge of humanoid robot teleoperation in a natural indoor environment via a Brain-Computer Interface (BCI). We leverage deep Convolutional Neural Network (CNN) based image and signal understanding to facilitate both real-... Read More about Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation.

Extended Patch Prioritization for Depth Filling Within Constrained Exemplar-Based RGB-D Image Completion (2018)
Presentation / Conference Contribution

We address the problem of hole filling in depth images, obtained from either active or stereo sensing, for the purposes of depth image completion in an exemplar-based framework. Most existing exemplar-based inpainting techniques, designed for color i... Read More about Extended Patch Prioritization for Depth Filling Within Constrained Exemplar-Based RGB-D Image Completion.

Order Matters: Shuffling Sequence Generation for Video Prediction (2019)
Presentation / Conference Contribution

Adaptive Visual-Depth Fusion Transfer (2018)
Presentation / Conference Contribution

Describing unseen classes by exemplars: Zero-shot learning using grouped simile ensemble (2017)
Presentation / Conference Contribution

Towards Universal Representation for Unseen Action Recognition (2018)
Presentation / Conference Contribution

Single Image Watermark Retrieval from 3D Printed Surfaces via Convolutional Neural Networks (2018)
Presentation / Conference Contribution

In this paper we propose and analyse a method for watermarking 3D printed objects, concentrating on the watermark retrieval problem. The method embeds the watermark in a planar region of the 3D printed object in the form of small semi-spherical or cu... Read More about Single Image Watermark Retrieval from 3D Printed Surfaces via Convolutional Neural Networks.

CAM: A Combined Attention Model for Natural Language Inference (2018)
Presentation / Conference Contribution

Natural Language Inference (NLI) is a fundamental step towards natural language understanding. The task aims to detect whether a premise entails or contradicts a given hypothesis. NLI contributes to a wide range of natural language understanding appl... Read More about CAM: A Combined Attention Model for Natural Language Inference.

Back to Butterworth - a Fourier Basis for 3D Surface Relief Hole Filling within RGB-D Imagery (2016)
Presentation / Conference Contribution

We address the problem of hole filling in RGB-D (color and depth) images, obtained from either active or stereo based sensing, for the purposes of object removal and missing depth estimation. This is performed independently on the low frequency depth... Read More about Back to Butterworth - a Fourier Basis for 3D Surface Relief Hole Filling within RGB-D Imagery.

Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments (2018)
Presentation / Conference Contribution

High Throughput Computing (HTC) provides a convenient mechanism for running thousands of tasks. Many HTC systems exploit computers which are provisioned for other purposes by utilising their idle time - volunteer computing. This has great advantages... Read More about Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments.

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training (2018)
Presentation / Conference Contribution

Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal). While... Read More about GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training.

Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior (2019)
Presentation / Conference Contribution

Monocular depth estimation using novel learning-based approaches has recently emerged as a promising potential alternative to more conventional 3D scene capture technologies within real-world scenarios. Many such solutions often depend on large quant... Read More about Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior.

Evaluating a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery (2019)
Presentation / Conference Contribution

X-ray baggage security screening is widely used to maintain aviation and transport secure. Of particular interestis the focus on automated security X-ray analysis for particular classes of object such as electronics, electrical items and liquids. How... Read More about Evaluating a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery.

Infrared Image Colorization Using S-Shape Network (2018)
Presentation / Conference Contribution

This paper proposes a novel approach for colorizing near infrared (NIR) images using a S-shape network (SNet). The proposed approach is based on the usage of an encoder-decoder architecture followed with a secondary assistant network. The encoder-dec... Read More about Infrared Image Colorization Using S-Shape Network.

Focus-tunable and fixed lenses and stereoscopic 3D displays (Conference Presentation) (2017)
Presentation / Conference Contribution

Stereoscopic 3D (S3D) displays provide an enhanced sense of depth by sending different images to the two eyes. But these displays do not reproduce focus cues (blur and accommodation) correctly. Specifically, the eyes must accommodate to the display s... Read More about Focus-tunable and fixed lenses and stereoscopic 3D displays (Conference Presentation).

Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification (2019)
Presentation / Conference Contribution

Despite significant recent progress in the area of Brain-Computer Interface (BCI), there are numerous shortcomings associated with collecting Electroencephalography (EEG) signals in real-world environments. These include, but are not limited to, subj... Read More about Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification.