Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Learning of Important Objects from First-Person Videos   A first-person camera, placed at a person's head, captures, which objects are
important to the camera wearer. Most prior methods for this task learn to
detect such important objects from the manually labeled first-person data in a
supervised fashion. However, important objects are strongly related to the
camera wearer's internal state such as his intentions and attention, and thus,
only the person wearing the camera can provide the importance labels. Such a
constraint makes the annotation process costly and limited in scalability.
  In this work, we show that we can detect important objects in first-person
images without the supervision by the camera wearer or even third-person
labelers. We formulate an important detection problem as an interplay between
the 1) segmentation and 2) recognition agents. The segmentation agent first
proposes a possible important object segmentation mask for each image, and then
feeds it to the recognition agent, which learns to predict an important object
mask using visual semantics and spatial features.
  We implement such an interplay between both agents via an alternating
cross-pathway supervision scheme inside our proposed Visual-Spatial Network
(VSN). Our VSN consists of spatial ("where") and visual ("what") pathways, one
of which learns common visual semantics while the other focuses on the spatial
location cues. Our unsupervised learning is accomplished via a cross-pathway
supervision, where one pathway feeds its predictions to a segmentation agent,
which proposes a candidate important object segmentation mask that is then used
by the other pathway as a supervisory signal. We show our method's success on
two different important object datasets, where our method achieves similar or
better results as the supervised methods.

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The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-ex