Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High-for-Low and Low-for-High: Efficient Boundary Detection from Deep
  Object Features and its Applications to High-Level Vision   Most of the current boundary detection systems rely exclusively on low-level
features, such as color and texture. However, perception studies suggest that
humans employ object-level reasoning when judging if a particular pixel is a
boundary. Inspired by this observation, in this work we show how to predict
boundaries by exploiting object-level features from a pretrained
object-classification network. Our method can be viewed as a "High-for-Low"
approach where high-level object features inform the low-level boundary
detection process. Our model achieves state-of-the-art performance on an
established boundary detection benchmark and it is efficient to run.
  Additionally, we show that due to the semantic nature of our boundaries we
can use them to aid a number of high-level vision tasks. We demonstrate that
using our boundaries we improve the performance of state-of-the-art methods on
the problems of semantic boundary labeling, semantic segmentation and object
proposal generation. We can view this process as a "Low-for-High" scheme, where
low-level boundaries aid high-level vision tasks.
  Thus, our contributions include a boundary detection system that is accurate,
efficient, generalizes well to multiple datasets, and is also shown to improve
existing state-of-the-art high-level vision methods on three distinct tasks.

29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg