Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recurrent Attention Models with Object-centric Capsule Representation
  for Multi-object Recognition   The visual system processes a scene using a sequence of selective glimpses,
each driven by spatial and object-based attention. These glimpses reflect what
is relevant to the ongoing task and are selected through recurrent processing
and recognition of the objects in the scene. In contrast, most models treat
attention selection and recognition as separate stages in a feedforward
process. Here we show that using capsule networks to create an object-centric
hidden representation in an encoder-decoder model with iterative glimpse
attention yields effective integration of attention and recognition. We
evaluate our model on three multi-object recognition tasks; highly overlapping
digits, digits among distracting clutter and house numbers, and show that it
learns to effectively move its glimpse window, recognize and reconstruct the
objects, all with only the classification as supervision. Our work takes a step
toward a general architecture for how to integrate recurrent object-centric
representation into the planning of attentional glimpses.

25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg