8 citations · 23 across the 13 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…