Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Parsing-based View-aware Embedding Network for Vehicle Re-Identification   Vehicle Re-Identification is to find images of the same vehicle from various
views in the cross-camera scenario. The main challenges of this task are the
large intra-instance distance caused by different views and the subtle
inter-instance discrepancy caused by similar vehicles. In this paper, we
propose a parsing-based view-aware embedding network (PVEN) to achieve the
view-aware feature alignment and enhancement for vehicle ReID. First, we
introduce a parsing network to parse a vehicle into four different views, and
then align the features by mask average pooling. Such alignment provides a
fine-grained representation of the vehicle. Second, in order to enhance the
view-aware features, we design a common-visible attention to focus on the
common visible views, which not only shortens the distance among
intra-instances, but also enlarges the discrepancy of inter-instances. The PVEN
helps capture the stable discriminative information of vehicle under different
views. The experiments conducted on three datasets show that our model
outperforms state-of-the-art methods by a large margin.

24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat