3 citations · 3 across the 3 of their papers we have counts for
5 papers
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings
Mennatullah Siam, Naren Doraiswamy, Boris N. Oreshkin +2
Significant progress has been made recently in developing few-shot object segmentation methods. Learning is shown to be successful in few-shot segmentation settings, using pixel-le…
One-Shot Weakly Supervised Video Object Segmentation
Mennatullah Siam, Naren Doraiswamy, Boris N. Oreshkin +2
Conventional few-shot object segmentation methods learn object segmentation from a few labelled support images with strongly labelled segmentation masks. Recent work has shown to p…
Adaptive Masked Proxies for Few-Shot Segmentation
Mennatullah Siam, Boris Oreshkin, Martin Jagersand
Deep learning has thrived by training on large-scale datasets. However, in robotics applications sample efficiency is critical. We propose a novel adaptive masked proxies method th…
Deep Semantic Segmentation for Automated Driving: Taxonomy, Roadmap and Challenges
Mennatullah Siam, Sara Elkerdawy, Martin Jagersand +1
Semantic segmentation was seen as a challenging computer vision problem few years ago. Due to recent advancements in deep learning, relatively accurate solutions are now possible f…
Parking Stall Vacancy Indicator System Based on Deep Convolutional Neural Networks
Sepehr Valipour, Mennatullah Siam, Eleni Stroulia +1
Parking management systems, and vacancy-indication services in particular, can play a valuable role in reducing traffic and energy waste in large cities. Visual detection methods r…