About the SIP Lab
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The Signal and Information Processing (SIP) Lab comprises faculty, post-doctoral researchers, Ph.D. and MSc students working in the broad area of signal/information processing and machine learning involving images, videos, and audio with applications to biometrics, security, forensics, and surveillance. Our research focus is on the following areas:
- Video analytics for surveillance and security
- Face analysis
- Emotion and behavior analysis
- Learning-based Visual Data Compression
Research at the SIP Lab is funded by various agencies from the UK, the European Union, and North America:
- European Commission - Marie Sklodowska-Curie Actions:
- Career Integration Grant
- Research and Innovation Staff Exchange (RISE)
- Engineering and Physical Sciences Research Council (EPSRC), UK
- Newton Fund International Collaboration Programme - Mexican Academy of Sciences
- Warwick Ventures, UK
- Ministry of Defence - Defence and Security Accelerator (DASA), UK
- Ford Motor Company, USA
- Research England through UUKi
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Contact
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Prof Victor Sanchez
Department of Computer Science, University of Warwick
Email: v.f.sanchez-silva at warwick.ac.uk
Tel: +44 (0) 24 7657 3887
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News
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- 21/12/2023 The paper "Overfitted Neural Networks for Block-based Intra-prediction" has been accepted to the 2024 Data Compression Conference (DCC)
- 13/12/2023 Congratulations to Haoyi Wang on his paper "Cross-Age Contrastive Learning for Age-Invariant Face Recognition" accepted to the 2024 Int. Conference on Acoustics, Speech and Signal Processing (ICASSP)
- 15/08/2023 The SIP Lab has been awarded funds by funds by the Defence and Security Accelerator (DASA) to develop technologies to detect anomalies in videos using online learning
- 01/04/2023 Congratulations to Yiming Ma on his paper "Robust Multiview Multimodal Driver Monitoring System Using Masked Multi-Head Self-Attention" accepted to the 2023 CVPR Workshop on Multi-Modal Learning and Applications
- 28/03/2023 The SIP Lab has been awarded funds by Research England through UUKi to develop computer vision technology to index, retrieve, and search images by content without the need for training models
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