Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Implementation of Faster RCNN with Study for Region Sampling   We adapted the join-training scheme of Faster RCNN framework from Caffe to
TensorFlow as a baseline implementation for object detection. Our code is made
publicly available. This report documents the simplifications made to the
original pipeline, with justifications from ablation analysis on both PASCAL
VOC 2007 and COCO 2014. We further investigated the role of non-maximal
suppression (NMS) in selecting regions-of-interest (RoIs) for region
classification, and found that a biased sampling toward small regions helps
performance and can achieve on-par mAP to NMS-based sampling when converged
sufficiently.

31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph