Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multiple instance learning on deep features for weakly supervised object
  detection with extreme domain shifts   Weakly supervised object detection (WSOD) using only image-level annotations
has attracted a growing attention over the past few years. Whereas such task is
typically addressed with a domain-specific solution focused on natural images,
we show that a simple multiple instance approach applied on pre-trained deep
features yields excellent performances on non-photographic datasets, possibly
including new classes. The approach does not include any fine-tuning or
cross-domain learning and is therefore efficient and possibly applicable to
arbitrary datasets and classes. We investigate several flavors of the proposed
approach, some including multi-layers perceptron and polyhedral classifiers.
Despite its simplicity, our method shows competitive results on a range of
publicly available datasets, including paintings (People-Art, IconArt),
watercolors, cliparts and comics and allows to quickly learn unseen visual
categories.

12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg