3 citations · 3 across the 4 of their papers we have counts for
10 papers · 1 filter
Generalized Few-Shot Semantic Segmentation in Remote Sensing: Challenge and Benchmark
Clifford Broni-Bediako, Junshi Xia, Jian Song +3
Learning with limited labelled data is a challenging problem in various applications, including remote sensing. Few-shot semantic segmentation is one approach that can encourage de…
Video Class Agnostic Segmentation with Contrastive Learning for Autonomous Driving
Mennatullah Siam, Alex Kendall, Martin Jagersand
Semantic segmentation in autonomous driving predominantly focuses on learning from large-scale data with a closed set of known classes without considering unknown objects. Motivate…
Video Class Agnostic Segmentation Benchmark for Autonomous Driving
Mennatullah Siam, Alex Kendall, Martin Jagersand
Semantic segmentation approaches are typically trained on large-scale data with a closed finite set of known classes without considering unknown objects. In certain safety-critical…
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…