8 citations · 23 across the 13 of their papers we have counts for
14 papers
Aries: A Proprietary Medium-Range Weather Prediction Model for the Energy Industry
Lukas Hedegaard Morsing, Arian Bakhtiarnia, Jonas Lynge Olesen +2
Medium-range weather forecasting underpins operational and planning decisions across the energy industry. Developing competitive weather models was once the domain of national mete…
Continual Low-Rank Scaled Dot-product Attention
Ginés Carreto Picón, Illia Oleksiienko, Lukas Hedegaard +2
Transformers are widely used for their ability to capture data relations in sequence processing, with great success for a wide range of static tasks. However, the computational and…
Are LLM-based methods good enough for detecting unfair terms of service?
Mirgita Frasheri, Arian Bakhtiarnia, Lukas Esterle +1
Countless terms of service (ToS) are being signed everyday by users all over the world while interacting with all kinds of apps and websites. More often than not, these online cont…
Audio-Visual Dataset and Method for Anomaly Detection in Traffic Videos
Błażej Leporowski, Arian Bakhtiarnia, Nicole Bonnici +4
We introduce the first audio-visual dataset for traffic anomaly detection taken from real-world scenes, called MAVAD, with a diverse range of weather and illumination conditions. I…
Accurate Gigapixel Crowd Counting by Iterative Zooming and Refinement
Arian Bakhtiarnia, Qi Zhang, Alexandros Iosifidis
The increasing prevalence of gigapixel resolutions has presented new challenges for crowd counting. Such resolutions are far beyond the memory and computation limits of current GPU…
PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks
Arian Bakhtiarnia, Qi Zhang, Alexandros Iosifidis
Many deep learning tasks require annotations that are too time consuming for human operators, resulting in small dataset sizes. This is especially true for dense regression problem…
Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training
Arian Bakhtiarnia, Qi Zhang, Alexandros Iosifidis
JPEG image compression algorithm is a widely used technique for image size reduction in edge and cloud computing settings. However, applying such lossy compression on images proces…
Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications
Arian Bakhtiarnia, Błażej Leporowski, Lukas Esterle +1
Images and video frames captured by cameras placed throughout smart cities are often transmitted over the network to a server to be processed by deep neural networks for various ta…
Efficient High-Resolution Deep Learning: A Survey
Arian Bakhtiarnia, Qi Zhang, Alexandros Iosifidis
Cameras in modern devices such as smartphones, satellites and medical equipment are capable of capturing very high resolution images and videos. Such high-resolution data often nee…
Dynamic Split Computing for Efficient Deep Edge Intelligence
Arian Bakhtiarnia, Nemanja Milošević, Qi Zhang +2
Deploying deep neural networks (DNNs) on IoT and mobile devices is a challenging task due to their limited computational resources. Thus, demanding tasks are often entirely offload…
Continual Transformers: Redundancy-Free Attention for Online Inference
Lukas Hedegaard, Arian Bakhtiarnia, Alexandros Iosifidis
Transformers in their common form are inherently limited to operate on whole token sequences rather than on one token at a time. Consequently, their use during online inference on…
Multi-Exit Vision Transformer for Dynamic Inference
Arian Bakhtiarnia, Qi Zhang, Alexandros Iosifidis
Deep neural networks can be converted to multi-exit architectures by inserting early exit branches after some of their intermediate layers. This allows their inference process to b…
Single-Layer Vision Transformers for More Accurate Early Exits with Less Overhead
Arian Bakhtiarnia, Qi Zhang, Alexandros Iosifidis
Deploying deep learning models in time-critical applications with limited computational resources, for instance in edge computing systems and IoT networks, is a challenging task th…
Improving the Accuracy of Early Exits in Multi-Exit Architectures via Curriculum Learning
Arian Bakhtiarnia, Qi Zhang, Alexandros Iosifidis
Deploying deep learning services for time-sensitive and resource-constrained settings such as IoT using edge computing systems is a challenging task that requires dynamic adjustmen…