108 citations · 839 across the 102 of their papers we have counts for
17 papers · 1 filter
A Deep Four-Stream Siamese Convolutional Neural Network with Joint Verification and Identification Loss for Person Re-detection
Amena Khatun, Simon Denman, Sridha Sridharan +1
State-of-the-art person re-identification systems that employ a triplet based deep network suffer from a poor generalization capability. In this paper, we propose a four stream Sia…
Multi-component Image Translation for Deep Domain Generalization
Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh +1
Domain adaption (DA) and domain generalization (DG) are two closely related methods which are both concerned with the task of assigning labels to an unlabeled data set. The only di…
GD-GAN: Generative Adversarial Networks for Trajectory Prediction and Group Detection in Crowds
Tharindu Fernando, Simon Denman, Sridha Sridharan +1
This paper presents a novel deep learning framework for human trajectory prediction and detecting social group membership in crowds. We introduce a generative adversarial pipeline…
Multi-Level Sequence GAN for Group Activity Recognition
Harshala Gammulle, Simon Denman, Sridha Sridharan +1
We propose a novel semi-supervised, Multi-Level Sequential Generative Adversarial Network (MLS-GAN) architecture for group activity recognition. In contrast to previous works which…
Component-based Attention for Large-scale Trademark Retrieval
Osman Tursun, Simon Denman, Sabesan Sivapalan +3
The demand for large-scale trademark retrieval (TR) systems has significantly increased to combat the rise in international trademark infringement. Unfortunately, the ranking accur…
Skeleton Driven Non-rigid Motion Tracking and 3D Reconstruction
Shafeeq Elanattil, Peyman Moghadam, Simon Denman +2
This paper presents a method which can track and 3D reconstruct the non-rigid surface motion of human performance using a moving RGB-D camera. 3D reconstruction of marker-less huma…
Pedestrian Trajectory Prediction with Structured Memory Hierarchies
Tharindu Fernando, Simon Denman, Sridha Sridharan +1
This paper presents a novel framework for human trajectory prediction based on multimodal data (video and radar). Motivated by recent neuroscience discoveries, we propose incorpora…
Semantic Correspondence: A Hierarchical Approach
Akila Pemasiri, Kien Nguyen, Sridha Sridhara +1
Establishing semantic correspondence across images when the objects in the images have undergone complex deformations remains a challenging task in the field of computer vision. In…
Sparse Over-complete Patch Matching
Akila Pemasiri, Kien Nguyen, Sridha Sridharan +1
Image patch matching, which is the process of identifying corresponding patches across images, has been used as a subroutine for many computer vision and image processing tasks. St…
Non-rigid Reconstruction with a Single Moving RGB-D Camera
Shafeeq Elanattil, Peyman Moghadam, Sridha Sridharan +2
We present a novel non-rigid reconstruction method using a moving RGB-D camera. Current approaches use only non-rigid part of the scene and completely ignore the rigid background.…
Meta Transfer Learning for Facial Emotion Recognition
Dung Nguyen, Kien Nguyen, Sridha Sridharan +3
The use of deep learning techniques for automatic facial expression recognition has recently attracted great interest but developed models are still unable to generalize well due t…
Deep Decision Trees for Discriminative Dictionary Learning with Adversarial Multi-Agent Trajectories
Tharindu Fernando, Sridha Sridharan, Clinton Fookes +1
With the explosion in the availability of spatio-temporal tracking data in modern sports, there is an enormous opportunity to better analyse, learn and predict important events in…
Learning Temporal Strategic Relationships using Generative Adversarial Imitation Learning
Tharindu Fernando, Simon Denman, Sridha Sridharan +1
This paper presents a novel framework for automatic learning of complex strategies in human decision making. The task that we are interested in is to better facilitate long term pl…
Learning Free-Form Deformations for 3D Object Reconstruction
Dominic Jack, Jhony K. Pontes, Sridha Sridharan +4
Representing 3D shape in deep learning frameworks in an accurate, efficient and compact manner still remains an open challenge. Most existing work addresses this issue by employing…
Task Specific Visual Saliency Prediction with Memory Augmented Conditional Generative Adversarial Networks
Tharindu Fernando, Simon Denman, Sridha Sridharan +1
Visual saliency patterns are the result of a variety of factors aside from the image being parsed, however existing approaches have ignored these. To address this limitation, we pr…
Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking
Tharindu Fernando, Simon Denman, Sridha Sridharan +1
Current multi-person localisation and tracking systems have an over reliance on the use of appearance models for target re-identification and almost no approaches employ a complete…
Deep Classification of Epileptic Signals
David Ahmedt-Aristizabal, Clinton Fookes, Kien Nguyen +1
Electrophysiological observation plays a major role in epilepsy evaluation. However, human interpretation of brain signals is subjective and prone to misdiagnosis. Automating this…