Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Instance Retrieval at Fine-grained Level Using Multi-Attribute
  Recognition   In this paper, we present a method for instance ranking and retrieval at
fine-grained level based on the global features extracted from a
multi-attribute recognition model which is not dependent on landmarks
information or part-based annotations. Further, we make this architecture
suitable for mobile-device application by adopting the bilinear CNN to make the
multi-attribute recognition model smaller (in terms of the number of
parameters). The experiments run on the Dress category of DeepFashion In-Shop
Clothes Retrieval and CUB200 datasets show that the results of instance
retrieval at fine-grained level are promising for these datasets, specially in
terms of texture and color.

31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph