108 citations · 839 across the 102 of their papers we have counts for
14 papers · 1 filter
Predicting the Future: A Jointly Learnt Model for Action Anticipation
Harshala Gammulle, Simon Denman, Sridha Sridharan +1
Inspired by human neurological structures for action anticipation, we present an action anticipation model that enables the prediction of plausible future actions by forecasting bo…
MTRNet++: One-stage Mask-based Scene Text Eraser
Osman Tursun, Simon Denman, Rui Zeng +3
A precise, controllable, interpretable and easily trainable text removal approach is necessary for both user-specific and large-scale text removal applications. To achieve this, we…
Correlation-aware Adversarial Domain Adaptation and Generalization
Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh +1
Domain adaptation (DA) and domain generalization (DG) have emerged as a solution to the domain shift problem where the distribution of the source and target data is different. The…
Exploiting Human Social Cognition for the Detection of Fake and Fraudulent Faces via Memory Networks
Tharindu Fernando, Clinton Fookes, Simon Denman +1
Advances in computer vision have brought us to the point where we have the ability to synthesise realistic fake content. Such approaches are seen as a source of disinformation and…
Neural Memory Plasticity for Anomaly Detection
Tharindu Fernando, Simon Denman, David Ahmedt-Aristizabal +4
In the domain of machine learning, Neural Memory Networks (NMNs) have recently achieved impressive results in a variety of application areas including visual question answering, tr…
Coupled Generative Adversarial Network for Continuous Fine-grained Action Segmentation
Harshala Gammulle, Tharindu Fernando, Simon Denman +2
We propose a novel conditional GAN (cGAN) model for continuous fine-grained human action segmentation, that utilises multi-modal data and learned scene context information. The pro…
Forecasting Future Action Sequences with Neural Memory Networks
Harshala Gammulle, Simon Denman, Sridha Sridharan +1
We propose a novel neural memory network based framework for future action sequence forecasting. This is a challenging task where we have to consider short-term, within sequence re…
Fine-grained Action Segmentation using the Semi-Supervised Action GAN
Harshala Gammulle, Simon Denman, Sridha Sridharan +1
In this paper we address the problem of continuous fine-grained action segmentation, in which multiple actions are present in an unsegmented video stream. The challenge for this ta…
Constrained Design of Deep Iris Networks
Kien Nguyen, Clinton Fookes, Sridha Sridharan
Despite the promise of recent deep neural networks in the iris recognition setting, there are vital properties of the classic IrisCode which are almost unable to be achieved with c…
Geometry-constrained Car Recognition Using a 3D Perspective Network
Rui Zeng, Zongyuan Ge, Simon Denman +2
We present a novel learning framework for vehicle recognition from a single RGB image. Unlike existing methods which only use attention mechanisms to locate 2D discriminative infor…
MTRNet: A Generic Scene Text Eraser
Osman Tursun, Rui Zeng, Simon Denman +3
Text removal algorithms have been proposed for uni-lingual scripts with regular shapes and layouts. However, to the best of our knowledge, a generic text removal method which is ab…
Robust Photogeometric Localization over Time for Map-Centric Loop Closure
Chanoh Park, Soohwan Kim, Peyman Moghadam +3
Map-centric SLAM is emerging as an alternative of conventional graph-based SLAM for its accuracy and efficiency in long-term mapping problems. However, in map-centric SLAM, the pro…
Memory Augmented Deep Generative models for Forecasting the Next Shot Location in Tennis
Tharindu Fernando, Simon Denman, Sridha Sridharan +1
This paper presents a novel framework for predicting shot location and type in tennis. Inspired by recent neuroscience discoveries we incorporate neural memory modules to model the…
On Minimum Discrepancy Estimation for Deep Domain Adaptation
Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh +1
In the presence of large sets of labeled data, Deep Learning (DL) has accomplished extraordinary triumphs in the avenue of computer vision, particularly in object classification an…