7 citations · 7 across the 2 of their papers we have counts for
5 papers
Find it if You Can: End-to-End Adversarial Erasing for Weakly-Supervised Semantic Segmentation
Erik Stammes, Tom F. H. Runia, Michael Hofmann +1
Semantic segmentation is a task that traditionally requires a large dataset of pixel-level ground truth labels, which is time-consuming and expensive to obtain. Recent advancements…
VATLD: A Visual Analytics System to Assess, Understand and Improve Traffic Light Detection
Liang Gou, Lincan Zou, Nanxiang Li +4
Traffic light detection is crucial for environment perception and decision-making in autonomous driving. State-of-the-art detectors are built upon deep Convolutional Neural Network…
I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation
Laurens Samson, Nanne van Noord, Olaf Booij +3
Adversarial training has been recently employed for realizing structured semantic segmentation, in which the aim is to preserve higher-level scene structural consistencies in dense…
Dynamic Adaptation on Non-Stationary Visual Domains
Sindi Shkodrani, Michael Hofmann, Efstratios Gavves
Domain adaptation aims to learn models on a supervised source domain that perform well on an unsupervised target. Prior work has examined domain adaptation in the context of statio…
EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection
Mohsen Ghafoorian, Cedric Nugteren, Nóra Baka +2
Convolutional neural networks have been successfully applied to semantic segmentation problems. However, there are many problems that are inherently not pixel-wise classification p…