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Found 11 papers, 3 papers with code
Audio-Visual Dataset and Method for Anomaly Detection in Traffic Videos
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.
Accurate Gigapixel Crowd Counting by Iterative Zooming and Refinement
The increasing prevalence of gigapixel resolutions has presented new challenges for crowd counting.
PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks
In this paper, we introduce PromptMix, a method for artificially boosting the size of existing datasets, that can be used to improve the performance of lightweight networks.
Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training
JPEG image compression algorithm is a widely used technique for image size reduction in edge and cloud computing settings.
Efficient High-Resolution Deep Learning: A Survey
Cameras in modern devices such as smartphones, satellites and medical equipment are capable of capturing very high resolution images and videos.
Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications
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 tasks.
Dynamic Split Computing for Efficient Deep Edge Intelligence
Split computing is a paradigm where a DNN is split into two sections; the first section is executed on the end device, and the output is transmitted to the edge server where the final section is executed.
Continual Transformers: Redundancy-Free Attention for Online Inference
Transformers in their common form are inherently limited to operate on whole token sequences rather than on one token at a time.
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Multi-Exit Vision Transformer for Dynamic Inference
In this work, we propose seven different architectures for early exit branches that can be used for dynamic inference in Vision Transformer backbones.
Single-Layer Vision Transformers for More Accurate Early Exits with Less Overhead
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 that often relies on dynamic inference methods such as early exiting.
Improving the Accuracy of Early Exits in Multi-Exit Architectures via Curriculum Learning
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 adjustment of inference time.
