Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Supervised Relative Depth Learning for Urban Scene Understanding   As an agent moves through the world, the apparent motion of scene elements is
(usually) inversely proportional to their depth. It is natural for a learning
agent to associate image patterns with the magnitude of their displacement over
time: as the agent moves, faraway mountains don't move much; nearby trees move
a lot. This natural relationship between the appearance of objects and their
motion is a rich source of information about the world. In this work, we start
by training a deep network, using fully automatic supervision, to predict
relative scene depth from single images. The relative depth training images are
automatically derived from simple videos of cars moving through a scene, using
recent motion segmentation techniques, and no human-provided labels. This proxy
task of predicting relative depth from a single image induces features in the
network that result in large improvements in a set of downstream tasks
including semantic segmentation, joint road segmentation and car detection, and
monocular (absolute) depth estimation, over a network trained from scratch. The
improvement on the semantic segmentation task is greater than those produced by
any other automatically supervised methods. Moreover, for monocular depth
estimation, our unsupervised pre-training method even outperforms supervised
pre-training with ImageNet. In addition, we demonstrate benefits from learning
to predict (unsupervised) relative depth in the specific videos associated with
various downstream tasks. We adapt to the specific scenes in those tasks in an
unsupervised manner to improve performance. In summary, for semantic
segmentation, we present state-of-the-art results among methods that do not use
supervised pre-training, and we even exceed the performance of supervised
ImageNet pre-trained models for monocular depth estimation, achieving results
that are comparable with state-of-the-art methods.

11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex