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Found 7 papers, 4 papers with code
Architecture Search of Dynamic Cells for Semantic Video Segmentation
In semantic video segmentation the goal is to acquire consistent dense semantic labelling across image frames.
Template-Based Automatic Search of Compact Semantic Segmentation Architectures
Automatic search of neural architectures for various vision and natural language tasks is becoming a prominent tool as it allows to discover high-performing structures on any dataset of interest.
Ranked #13 on
Semantic Segmentation
on CamVid
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
While most results in this domain have been achieved on image classification and language modelling problems, here we concentrate on dense per-pixel tasks, in particular, semantic image segmentation using fully convolutional networks.
Ranked #13 on
Semantic Segmentation
on PASCAL VOC 2012 val
Light-Weight RefineNet for Real-Time Semantic Segmentation
We consider an important task of effective and efficient semantic image segmentation.
Ranked #2 on
Real-Time Semantic Segmentation
on NYU Depth v2
Diagnostics in Semantic Segmentation
Over the past years, computer vision community has contributed to enormous progress in semantic image segmentation, a per-pixel classification task, crucial for dense scene understanding and rapidly becoming vital in lots of real-world applications, including driverless cars and medical imaging.
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations
Deployment of deep learning models in robotics as sensory information extractors can be a daunting task to handle, even using generic GPU cards.
Ranked #6 on
Real-Time Semantic Segmentation
on NYU Depth v2
Global Deconvolutional Networks for Semantic Segmentation
Semantic image segmentation is a principal problem in computer vision, where the aim is to correctly classify each individual pixel of an image into a semantic label.
