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Research funding

• 2026 -2027. Twinning Dual Degree Warwick-NURE
  Funder: Cormack Consultancy Group (CCG) and Mosaic Education
  Description: Support of a dual MSc degree between Kharkiv National University of Radio Electronics, NURE, Ukraine, and the University of Warwick, Department of Computer Science
• 2026 – 2034. Sub-lot 3.7: Understanding and interpretation of information for environmental acoustics
  Funder: Environmental Science Research, Development and Evidence (ES RDE) Framework
  Description: Development of AI tools to discover and interpret common patterns in acoustic information
• 2023 – 2024. O-VAD: Video Anomaly Detection without Offline Training
  Funder: Defence and Security Accelerator (DASA), UK
  Description: Development of deep learning technology to detect anomalies in videos using online training

• 2023 – 2024. TinyFaces: Real-time detection and clustering of small faces in videos
  Funder: Defence and Security Accelerator (DASA), UK
  Description: Development of deep learning technology to detect small faces in videos and cluster them by similarity
• 2023. REWARD: Radio Electronics-Warwick Allied Research and Development
  Partner: Kharkiv National University of Radio Electronics, NURE, Ukraine
  Funder: Research England through UUKi
  Description: Development of computer vision technology to index, retrieve, and search images by content without the need for training models.
• 2021 – 2023. Multimodal Learning for In-Car Driver Activity Monitoring
  Funder: Ford University Research Program, USA
  Description: Development of deep learning technology to monitor drivers' distraction using multisensor input
• 2019 – 2021. R-DIPS: Real-time Detection of Concealment of Intent for Passenger Screening – Phase 1 & Phase 2
  Funder: Defence and Security Accelerator (DASA), UK
  Description: Development of computer vision technologies to detect suspicious behaviors and movent at airports
• 2019. Spatiotemporal Binarization for Video Analytics using Machine Learning 
  Funder: Industrial Award, Warwick Ventures, UK
  Description: Development of a commercial system for the detection and visualization of unusual events in surveillance videos using binary feature descriptors
• 2019. Crossmodal identity matching
  Funder: Research Development Award, University of Warwick
  Description: Development of deep learning models to match identities across modalities and databases, for example, using voice to match a face and vice versa.
• 2016 – 2019. IDENTITY: Computer Vision Enabled Multimedia Forensics and People IdentificationLink opens in a new window
  Funder: Marie Sklodowska-Curie Actions - Research and Innovation Staff Exchange, European Union
  Description: Development of technology for digital forensics and biometrics using deep/machine learning and computer vision. Transfer knowledge activities within the academic and industrial partners of the consortium
• 2017. ISeC - Intelligent and Secure Cities
  Funder: Mobility Grant - Newton Fund International Collaboration Programme, Mexico-UK
  Description: Development of computer vision technology for people identification and unusual event detection using video data
• 2016 – 2017. MIUSS: Machine Intelligence in Urban Security Systems
  Funder: Engineering and Physical Sciences Research Council (EPSRC), UK, Institutional Award
  Description: Development of computer vision technology for unusual event detection using video captured by surveillance cameras
• 2014 – 2016. Coding of big pathology imaging data for storage, access, and transmission
  Funder: Engineering and Physical Sciences Research Council (EPSRC), UK, First Grant Scheme
  Description: Development of visualization and data analysis tools for high-resolution pathology images
• 2013 – 2017. PIMCO: Pathology Image Coding
  Funder: Marie Curie Career Integration Grant, European Union
  Description: Development of analysis, coding, and visualization tools for high-resolution pathology images using computer vision and machine learning
• 2012 – 2013. Coding of big pathology imaging data
  Funder: Research Development Award, University of Warwick
  Description: Development of coding methods for high-resolution pathology images

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Research areas

Detection and analysis of synthetic imaging data
Generative Adversarial Networks are widely used to generate synthetic face images.	The latest advances in generative AI have enabled the creation of synthetic media that are indistinguishable from authentic content. We are developing several AI-based solutions not only to detect deepfakes, but also to understand how this synthetic data are created. We are currently investigating the following topics: Detection of synthetic videos created by Text-To-Video (T2V) and Image-To-Video (I2V) generative models Development of explainable AI to understand how the training data is embedded into synthetic images created by diffusion models Anomaly detection techniques for deepfake detection Detection of morph attacks to face recognition systems using probabilistic models and unsupervised learning
Video analytics for surveillance and security
Object trajectory forecasting: What will the pedestrian do next?	Big data continues to grow exponentially, and surveillance video has become one of the largest sources. The video data acquired by existing large networks of cameras introduces many technological challenges, including storage, transmission, and analysis. Among these, one of the most critical challenges is how to intelligently analyze and understand the visual information, e.g., for activity recognition and anomaly detection. We are developing machine-learning solutions to improve the analysis and understanding of video data. We are currently investigating the following topics: Object trajectory forecasting Anomaly detection
Face analytics
A demonstration of face ageing. The top row depicts images generated by a vanilla Generative Adversarial Network suffering from the mode collapse issue. The bottom row depicts images generated by our proposed methods.	Face is our primary identity. Facial attributes, such as age and expressions, also convey a wealth of social signals. Computational analysis, modelling, and recognition of faces are thus critical in many domains, including person identification, human-robot interaction, animation, and mental health. We are currently investigating the following topics: Pose-invariant face recognition Age-Invariant face recognition Face recognition based on synthetic images Facial age estimation Facial expression recognition Face clustering
Semi-supervised learning
Improvement of the diversity and representation of tokens (embeddings) for fine-grained visual classification. The figure illustrates how our proposed method consistently highlights multiple semantically relevant regions within each object.	Semi-supervised learning (SSL) aims to mitigate the scarcity of large-scale labeled training data while reducing annotation costs. Several SSL models are trained under the assumption that access to a dataset with a balanced number of labeled samples per class is possible. In practice, label imbalance induces learning biases in SSL, hindering performance. We are currently investigating the following topics: Leveraging Vision Mamba for SSL in image classification tasks Improved diversity and representation in SSL at the token representation level SSL for fine-grained visual classification

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