51 research outputs found
Benchmarking the Robustness of Semantic Segmentation Models
When designing a semantic segmentation module for a practical application,
such as autonomous driving, it is crucial to understand the robustness of the
module with respect to a wide range of image corruptions. While there are
recent robustness studies for full-image classification, we are the first to
present an exhaustive study for semantic segmentation, based on the
state-of-the-art model DeepLabv3+. To increase the realism of our study, we
utilize almost 400,000 images generated from Cityscapes, PASCAL VOC 2012, and
ADE20K. Based on the benchmark study, we gain several new insights. Firstly,
contrary to full-image classification, model robustness increases with model
performance, in most cases. Secondly, some architecture properties affect
robustness significantly, such as a Dense Prediction Cell, which was designed
to maximize performance on clean data only.Comment: CVPR 2020 camera read
BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images
Current state-of-the-art segmentation techniques for ocular images are
critically dependent on large-scale annotated datasets, which are
labor-intensive to gather and often raise privacy concerns. In this paper, we
present a novel framework, called BiOcularGAN, capable of generating synthetic
large-scale datasets of photorealistic (visible light and near-infrared) ocular
images, together with corresponding segmentation labels to address these
issues. At its core, the framework relies on a novel Dual-Branch StyleGAN2
(DB-StyleGAN2) model that facilitates bimodal image generation, and a Semantic
Mask Generator (SMG) component that produces semantic annotations by exploiting
latent features of the DB-StyleGAN2 model. We evaluate BiOcularGAN through
extensive experiments across five diverse ocular datasets and analyze the
effects of bimodal data generation on image quality and the produced
annotations. Our experimental results show that BiOcularGAN is able to produce
high-quality matching bimodal images and annotations (with minimal manual
intervention) that can be used to train highly competitive (deep) segmentation
models (in a privacy aware-manner) that perform well across multiple real-world
datasets. The source code for the BiOcularGAN framework is publicly available
at https://github.com/dariant/BiOcularGAN.Comment: 13 pages, 14 figure
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