111 citations · 112 across the 4 of their papers we have counts for
7 papers
Architecture Search of Dynamic Cells for Semantic Video Segmentation
Vladimir Nekrasov, Hao Chen, Chunhua Shen +1
In semantic video segmentation the goal is to acquire consistent dense semantic labelling across image frames. To this end, recent approaches have been reliant on manually arranged…
Template-Based Automatic Search of Compact Semantic Segmentation Architectures
Vladimir Nekrasov, Chunhua Shen, Ian Reid
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 datas…
Light-Weight RefineNet for Real-Time Semantic Segmentation
Vladimir Nekrasov, Chunhua Shen, Ian Reid
We consider an important task of effective and efficient semantic image segmentation. In particular, we adapt a powerful semantic segmentation architecture, called RefineNet, into…
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
Vladimir Nekrasov, Hao Chen, Chunhua Shen +1
Automated design of neural network architectures tailored for a specific task is an extremely promising, albeit inherently difficult, avenue to explore. While most results in this…
Diagnostics in Semantic Segmentation
Vladimir Nekrasov, Chunhua Shen, Ian Reid
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 underst…
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations
Vladimir Nekrasov, Thanuja Dharmasiri, Andrew Spek +3
Deployment of deep learning models in robotics as sensory information extractors can be a daunting task to handle, even using generic GPU cards. Here, we address three of its most…
Global Deconvolutional Networks for Semantic Segmentation
Vladimir Nekrasov, Janghoon Ju, Jaesik Choi
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. Its widesprea…