Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: REAPS: Towards Better Recognition of Fine-grained Images by Region
  Attending and Part Sequencing   Fine-grained image recognition has been a hot research topic in computer
vision due to its various applications. The-state-of-the-art is the
part/region-based approaches that first localize discriminative parts/regions,
and then learn their fine-grained features. However, these approaches have some
inherent drawbacks: 1) the discriminative feature representation of an object
is prone to be disturbed by complicated background; 2) it is unreasonable and
inflexible to fix the number of salient parts, because the intended parts may
be unavailable under certain circumstances due to occlusion or incompleteness,
and 3) the spatial correlation among different salient parts has not been
thoroughly exploited (if not completely neglected). To overcome these
drawbacks, in this paper we propose a new, simple yet robust method by building
part sequence model on the attended object region. Concretely, we first try to
alleviate the background effect by using a region attention mechanism to
generate the attended region from the original image. Then, instead of
localizing different salient parts and extracting their features separately, we
learn the part representation implicitly by applying a mapping function on the
serialized features of the object. Finally, we combine the region attending
network and the part sequence learning network into a unified framework that
can be trained end-to-end with only image-level labels. Our extensive
experiments on three fine-grained benchmarks show that the proposed method
achieves the state of the art performance.

18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat