(N00014-16-R-FO05) Semantic Information Pursuit for Multimodal Data Analysis
Lead Research Organisation:
University of Surrey
Department Name: Vision Speech and Signal Proc CVSSP
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Publications
Ahmed S
(2021)
Deep Convolutional Neural Network Ensembles Using ECOC
in IEEE Access
Akbari A
(2023)
A Theoretical Insight Into the Effect of Loss Function for Deep Semantic-Preserving Learning
in IEEE Transactions on Neural Networks and Learning Systems
Akbari A
(2022)
A Novel Ground Metric for Optimal Transport-Based Chronological Age Estimation.
in IEEE transactions on cybernetics
Akbari A
(2022)
Distribution Cognisant Loss for Cross-Database Facial Age Estimation With Sensitivity Analysis.
in IEEE transactions on pattern analysis and machine intelligence
Akbari A
(2022)
RAgE: Robust Age Estimation Through Subject Anchoring with Consistency Regularisation.
in IEEE transactions on pattern analysis and machine intelligence
Arashloo SR
(2021)
Robust One-Class Kernel Spectral Regression.
in IEEE transactions on neural networks and learning systems
Bashar M
(2020)
Exploiting Deep Learning in Limited-Fronthaul Cell-Free Massive MIMO Uplink
in IEEE Journal on Selected Areas in Communications
Chen Z
(2020)
Low-rank discriminative least squares regression for image classification
in Signal Processing
Chen Z
(2021)
Learning Alternating Deep-Layer Cascaded Representation
in IEEE Signal Processing Letters
Chen Z
(2022)
Relaxed Block-Diagonal Dictionary Pair Learning With Locality Constraint for Image Recognition.
in IEEE transactions on neural networks and learning systems
Chen Z
(2023)
Fisher Regularized e-Dragging for Image Classification
in IEEE Transactions on Cognitive and Developmental Systems
Chen Z
(2019)
A sparse regularized nuclear norm based matrix regression for face recognition with contiguous occlusion
in Pattern Recognition Letters
Chen Z
(2023)
Hybrid Riemannian Graph-Embedding Metric Learning for Image Set Classification
in IEEE Transactions on Big Data
Chen Z
(2022)
Discriminative Dictionary Pair Learning With Scale-Constrained Structured Representation for Image Classification.
in IEEE transactions on neural networks and learning systems
Dong W
(2019)
Sparse subspace clustering via smoothed l minimization
in Pattern Recognition Letters
Dong W
(2019)
Sparse subspace clustering via nonconvex approximation
in Pattern Analysis and Applications
Dos Santos FP
(2020)
Learning image features with fewer labels using a semi-supervised deep convolutional network.
in Neural networks : the official journal of the International Neural Network Society
Farooq A
(2020)
Cross Modal Person Re-identification with Visual-Textual Queries
Fatemifar S
(2021)
Client-specific anomaly detection for face presentation attack detection
in Pattern Recognition
Fatemifar S
(2019)
Spoofing Attack Detection by Anomaly Detection
Fatemifar S
(2022)
Developing a generic framework for anomaly detection
in Pattern Recognition
Feng Z
(2019)
Rectified Wing Loss for Efficient and Robust Facial Landmark Localisation with Convolutional Neural Networks
in International Journal of Computer Vision
Feng Z
(2019)
Mining Hard Augmented Samples for Robust Facial Landmark Localization With CNNs
in IEEE Signal Processing Letters
Guo Q
(2020)
Self-grouping convolutional neural networks.
in Neural networks : the official journal of the International Neural Network Society
Hainy M
(2022)
Optimal Bayesian design for model discrimination via classification.
in Statistics and computing
Hu C
(2019)
Semi-Supervised Learning Based on GAN With Mean and Variance Feature Matching
in IEEE Transactions on Cognitive and Developmental Systems
Hu C
(2020)
Dual Encoder-Decoder Based Generative Adversarial Networks for Disentangled Facial Representation Learning
in IEEE Access
Huang Z
(2018)
Articulated Motion and Deformable Objects
Jiang L
(2019)
Pose-invariant three-dimensional face reconstruction
in Journal of Electronic Imaging
Khalid S
(2020)
Resolution Invariant Face Recognition Using a Distillation Approach
in IEEE Transactions on Biometrics, Behavior, and Identity Science
Khalid SS
(2022)
NPT-Loss: Demystifying face recognition losses with Nearest Proxies Triplet.
in IEEE transactions on pattern analysis and machine intelligence
Kittler J
(2019)
Delta Divergence: A Novel Decision Cognizant Measure of Classifier Incongruence.
in IEEE transactions on cybernetics
Li H
(2023)
LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images.
in IEEE transactions on pattern analysis and machine intelligence
Li H
(2020)
MDLatLRR: A novel decomposition method for infrared and visible image fusion.
in IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Li Y
(2019)
L1-2D2PCANet: a deep learning network for face recognition
in Journal of Electronic Imaging
| Description | The project developed state of the art algorithms for visual object tracking, landmark retrieval from large databases, and information theory measures for deep distribution learning, multidimensional regression and object classification. |
| Exploitation Route | The outcomes are relevant for many diverse artificial intelligence applications. |
| Sectors | Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Electronics,Healthcare,Leisure Activities, including Sports, Recreation and Tourism,Government, Democracy and Justice,Retail,Security and Diplomacy,Transport |
| Description | Some of the findings are commercialised by a University spinout, Sensus Futuris. |
| First Year Of Impact | 2022 |
| Sector | Digital/Communication/Information Technologies (including Software) |
| Impact Types | Economic |
| Description | Multimodal Video Search by Examples (MVSE) |
| Amount | £863,564 (GBP) |
| Funding ID | EP/V002856/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 09/2020 |
| End | 09/2023 |
| Description | Anomaly detection |
| Organisation | Bilkent University |
| Country | Turkey |
| Sector | Academic/University |
| PI Contribution | Joint research on one class classification methods for applications to anomaly detection |
| Collaborator Contribution | The kernel null-space technique and its regression-based formulation (called one-class kernel spectral regression, a.k.a. OC-KSR) is known to be an effective and computationally attractive one-class classification framework. Despite its outstanding performance, the applicability of kernel null-space method is limited due to its susceptibility to possible training data corruptions and inability to rank training observations according to their conformity with the model. This work addresses these shortcomings by studying the effect of regularising the solution of the null-space kernel Fisher methodology in the context of its regression-based formulation (OC-KSR). In this respect, first, the effect of a Tikhonov regularisation in the Hilbert space is analysed where the one-class learning problem in presence of contaminations in the training set is posed as a sensitivity analysis problem. Next, driven by the success of the sparse representation methodology, the effect of a sparsity regularisation on the solution is studied. For both alternative regularisation schemes, iterative algorithms are proposed which recursively update label confidences and rank training observations based on their fit with the model. Through extensive experiments conducted on different data sets, the proposed methodology is found to enhance robustness against contaminations in the training set as compared with the baseline kernel null-space technique as well as other existing approaches in a one-class classification paradigm while providing the functionality to rank training samples effectively. |
| Impact | Paper submitted for publication |
| Start Year | 2018 |
| Description | International Joint Laboratory for Pattern Recognition and Computational Intelligence |
| Organisation | Jianghan University |
| Country | China |
| Sector | Academic/University |
| PI Contribution | Supervising visiting PhD students/researchers from Jiangnan University, visiting Jiangnan University to collaborate with the local research team. |
| Collaborator Contribution | Working on joint problems in machine learning. |
| Impact | See the Publications section |
| Start Year | 2016 |
| Company Name | SENSUS FUTURIS LTD |
| Description | Artificial Intelligence and Machine Learning solutions for security, retail, advertising, healthcare and wellbeing industry. |
| Year Established | 2018 |
| Impact | Product development stage |
| Website | http://sensusfuturis.com |
| Description | Biometrics technology demonstration |
| Form Of Engagement Activity | Participation in an open day or visit at my research institution |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Industry/Business |
| Results and Impact | This was an open day organised by the Centre for Vision, Speech and Signal Processing on the occasion of the 30th anniversary of its founding. The research and technologies developed at the Centre were demonstrated to circa 250 attendees, mainly form industry. |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://www.surrey.ac.uk/centre-vision-speech-signal-processing/about/30th-anniversary |
| Description | CAIEP-UK Seminar of Foreign Experts |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Policymakers/politicians |
| Results and Impact | This seminar was hosted in London by the State Administration of Foreign Affairs, China to solicit feedback on various aspects of scientific endeavour in China. The feedback may impact on future cooperation in the field of science and technology between China and the UK. |
| Year(s) Of Engagement Activity | 2018 |
| Description | Invited keynote talk on Quantifying Semantic Information, presented at the IAPR S+SSPR 2018 Workshop |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Josef Kittler presented an invite keynote talk on Quantifying Semantic Information, at the International Association for Pattern Recognition Workshop on Statistical, Structural and Syntactic Pattern Recognition, held at Fragrance Hill, Beijing, China on August 17-19, 2018. |
| Year(s) Of Engagement Activity | 2018 |
| URL | http://ssspr2018.buaa.edu.cn |
| Description | ORBIT Seminar: Responsible Innovation for Industry - An Introduction to PAS 440 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | An RBIT seminar on responsible engineering. |
| Year(s) Of Engagement Activity | 2020 |
| URL | http://www.orbit-rri.org |
| Description | WISE 2019 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Professor Kittler presented an invited talk on aspects of Artificial Intelligence at the Westlake International Symposium 2019. The title of his talk was: The changing face of the curse of dimensionality reduction in machine learning. |
| Year(s) Of Engagement Activity | 2019 |