Search Results for author:
Found 7 papers, 3 papers with code
Budget-Aware Adapters for Multi-Domain Learning
To implement this idea we derive specialized deep models for each domain by adapting a pre-trained architecture but, differently from other methods, we propose a novel strategy to automatically adjust the computational complexity of the network.
PolyLaneNet: Lane Estimation via Deep Polynomial Regression
One of the main factors that contributed to the large advances in autonomous driving is the advent of deep learning.
Ranked #9 on
Lane Detection
on LLAMAS
Deep Traffic Sign Detection and Recognition Without Target Domain Real Images
The method does not aim at overcoming the training with real data, but to be a compatible alternative when the real data is not available.
Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection
Modern lane detection methods have achieved remarkable performances in complex real-world scenarios, but many have issues maintaining real-time efficiency, which is important for autonomous vehicles.
Ranked #6 on
Lane Detection
on LLAMAS
Deep Learning-based Type Identification of Volumetric MRI Sequences
The analysis of Magnetic Resonance Imaging (MRI) sequences enables clinical professionals to monitor the progression of a brain tumor.
Budget-Aware Pruning for Multi-Domain Learning
Nevertheless, the models are usually larger than the baseline for a single domain.
Budget-Aware Pruning: Handling Multiple Domains with Less Parameters
Nevertheless, the models are usually larger than the baseline for a single domain.
