44,783 research outputs found
Detecting the Unexpected via Image Resynthesis
Classical semantic segmentation methods, including the recent deep learning
ones, assume that all classes observed at test time have been seen during
training. In this paper, we tackle the more realistic scenario where unexpected
objects of unknown classes can appear at test time. The main trends in this
area either leverage the notion of prediction uncertainty to flag the regions
with low confidence as unknown, or rely on autoencoders and highlight
poorly-decoded regions. Having observed that, in both cases, the detected
regions typically do not correspond to unexpected objects, in this paper, we
introduce a drastically different strategy: It relies on the intuition that the
network will produce spurious labels in regions depicting unexpected objects.
Therefore, resynthesizing the image from the resulting semantic map will yield
significant appearance differences with respect to the input image. In other
words, we translate the problem of detecting unknown classes to one of
identifying poorly-resynthesized image regions. We show that this outperforms
both uncertainty- and autoencoder-based methods
Segmentation of surface cracks based on a fully convolutional neural network and gated scale pooling
Machine-Assisted Map Editing
Mapping road networks today is labor-intensive. As a result, road maps have
poor coverage outside urban centers in many countries. Systems to automatically
infer road network graphs from aerial imagery and GPS trajectories have been
proposed to improve coverage of road maps. However, because of high error
rates, these systems have not been adopted by mapping communities. We propose
machine-assisted map editing, where automatic map inference is integrated into
existing, human-centric map editing workflows. To realize this, we build
Machine-Assisted iD (MAiD), where we extend the web-based OpenStreetMap editor,
iD, with machine-assistance functionality. We complement MAiD with a novel
approach for inferring road topology from aerial imagery that combines the
speed of prior segmentation approaches with the accuracy of prior iterative
graph construction methods. We design MAiD to tackle the addition of major,
arterial roads in regions where existing maps have poor coverage, and the
incremental improvement of coverage in regions where major roads are already
mapped. We conduct two user studies and find that, when participants are given
a fixed time to map roads, they are able to add as much as 3.5x more roads with
MAiD
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