Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FeatureNMS: Non-Maximum Suppression by Learning Feature Embeddings   Most state of the art object detectors output multiple detections per object.
The duplicates are removed in a post-processing step called Non-Maximum
Suppression. Classical Non-Maximum Suppression has shortcomings in scenes that
contain objects with high overlap: This heuristic assumes that a high overlap
between two bounding boxes corresponds to a high probability of one being a
duplicate. We propose FeatureNMS to solve this problem. FeatureNMS recognizes
duplicates not only based on the intersection over union between the bounding
boxes, but also based on the difference of feature vectors. These feature
vectors can encode more information like visual appearance. Our approach
outperforms classical NMS and derived approaches and achieves state of the art
performance.

27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga