Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image
  Retrieval   Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of
retrieving a particular photo instance given a user's query sketch. Its
widespread applicability is however hindered by the fact that drawing a sketch
takes time, and most people struggle to draw a complete and faithful sketch. In
this paper, we reformulate the conventional FG-SBIR framework to tackle these
challenges, with the ultimate goal of retrieving the target photo with the
least number of strokes possible. We further propose an on-the-fly design that
starts retrieving as soon as the user starts drawing. To accomplish this, we
devise a reinforcement learning-based cross-modal retrieval framework that
directly optimizes rank of the ground-truth photo over a complete sketch
drawing episode. Additionally, we introduce a novel reward scheme that
circumvents the problems related to irrelevant sketch strokes, and thus
provides us with a more consistent rank list during the retrieval. We achieve
superior early-retrieval efficiency over state-of-the-art methods and
alternative baselines on two publicly available fine-grained sketch retrieval
datasets.

37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex