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Found 9 papers, 2 papers with code
Study of Subjective and Objective Quality in Super-Resolution Enhanced Broadcast Images on a Novel SR-IQA Dataset
To display low-quality broadcast content on high-resolution screens in full-screen format, the application of Super-Resolution (SR), a key consumer technology, is essential.
Speed Is All You Need: On-Device Acceleration of Large Diffusion Models via GPU-Aware Optimizations
The rapid development and application of foundation models have revolutionized the field of artificial intelligence.
Efficient Heterogeneous Video Segmentation at the Edge
We introduce an efficient video segmentation system for resource-limited edge devices leveraging heterogeneous compute.
Deep learning-based framework for cardiac function assessment in embryonic zebrafish from heart beating videos
Zebrafish is a powerful and widely-used model system for a host of biological investigations including cardiovascular studies and genetic screening.
Efficient Memory Management for Deep Neural Net Inference
While deep neural net inference was considered a task for servers only, latest advances in technology allow the task of inference to be moved to mobile and embedded devices, desired for various reasons ranging from latency to privacy.
Unconstrained Road Marking Recognition with Generative Adversarial Networks
Recent road marking recognition has achieved great success in the past few years along with the rapid development of deep learning.
SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition
In this paper, we present an algorithm for real-world license plate recognition (LPR) from a low-quality image.
Ranked #1 on
License Plate Recognition
on AOLP-RP
On-Device Neural Net Inference with Mobile GPUs
On-device inference of machine learning models for mobile phones is desirable due to its lower latency and increased privacy.
MediaPipe: A Framework for Building Perception Pipelines
A developer can use MediaPipe to build prototypes by combining existing perception components, to advance them to polished cross-platform applications and measure system performance and resource consumption on target platforms.
Distributed, Parallel, and Cluster Computing
