Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Grid R-CNN   This paper proposes a novel object detection framework named Grid R-CNN,
which adopts a grid guided localization mechanism for accurate object
detection. Different from the traditional regression based methods, the Grid
R-CNN captures the spatial information explicitly and enjoys the position
sensitive property of fully convolutional architecture. Instead of using only
two independent points, we design a multi-point supervision formulation to
encode more clues in order to reduce the impact of inaccurate prediction of
specific points. To take the full advantage of the correlation of points in a
grid, we propose a two-stage information fusion strategy to fuse feature maps
of neighbor grid points. The grid guided localization approach is easy to be
extended to different state-of-the-art detection frameworks. Grid R-CNN leads
to high quality object localization, and experiments demonstrate that it
achieves a 4.1% AP gain at IoU=0.8 and a 10.0% AP gain at IoU=0.9 on COCO
benchmark compared to Faster R-CNN with Res50 backbone and FPN architecture.

29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci