4 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
Detecting Road Obstacles by Erasing Them
Vehicles can encounter a myriad of obstacles on the road, and it is not
feasible to record them all beforehand to train a detector. Our method selects
image patches and inpaints them with the surrounding road texture, which tends
to remove obstacles from those patches. It them uses a network trained to
recognize discrepancies between the original patch and the inpainted one, which
signals an erased obstacle.
We also contribute a new dataset for monocular road obstacle detection, and
show that our approach outperforms the state-of-the-art methods on both our new
dataset and the standard Fishyscapes Lost & Found benchmark