Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FineTag: Multi-attribute Classification at Fine-grained Level in Images   In this paper, we address the extraction of the fine-grained attributes of an
instance as a `multi-attribute classification' problem. To this end, we propose
an end-to-end architecture by adopting the bi-linear Convolutional Neural
Network with the pairwise ranking loss. This is the first time such
architecture is applied for the fine-grained attributes classification problem.
We compared the proposed method with a competitive deep Convolutional Neural
Network baseline. Extensive experiments show that the proposed method
attains/outperforms the performance of compared baseline with significantly
less number of parameters ($40\times$ less). We demonstrated our approach on
CUB200 birds dataset whose annotations are adapted in this work for
multi-attribute classification at fine-grained level.

31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con