176 citations · 274 across the 36 of their papers we have counts for
6 papers · 1 filter
Fast Panoptic Segmentation Network
Daan de Geus, Panagiotis Meletis, Gijs Dubbelman
In this work, we present an end-to-end network for fast panoptic segmentation. This network, called Fast Panoptic Segmentation Network (FPSNet), does not require computationally co…
Semantic Foreground Inpainting from Weak Supervision
Chenyang Lu, Gijs Dubbelman
Semantic scene understanding is an essential task for self-driving vehicles and mobile robots. In our work, we aim to estimate a semantic segmentation map, in which the foreground…
Data Selection for training Semantic Segmentation CNNs with cross-dataset weak supervision
Panagiotis Meletis, Rob Romijnders, Gijs Dubbelman
Training convolutional networks for semantic segmentation with strong (per-pixel) and weak (per-bounding-box) supervision requires a large amount of weakly labeled data. We propose…
Hallucinating Beyond Observation: Learning to Complete with Partial Observation and Unpaired Prior Knowledge
Chenyang Lu, Gijs Dubbelman
We propose a novel single-step training strategy that allows convolutional encoder-decoder networks that use skip connections, to complete partially observed data by means of hallu…
On Boosting Semantic Street Scene Segmentation with Weak Supervision
Panagiotis Meletis, Gijs Dubbelman
Training convolutional networks for semantic segmentation requires per-pixel ground truth labels, which are very time consuming and hence costly to obtain. Therefore, in this work,…
Single Network Panoptic Segmentation for Street Scene Understanding
Daan de Geus, Panagiotis Meletis, Gijs Dubbelman
In this work, we propose a single deep neural network for panoptic segmentation, for which the goal is to provide each individual pixel of an input image with a class label, as in…