Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Diverse and Accurate Image Captions via Reinforcing
  Determinantal Point Process   Although significant progress has been made in the field of automatic image
captioning, it is still a challenging task. Previous works normally pay much
attention to improving the quality of the generated captions but ignore the
diversity of captions. In this paper, we combine determinantal point process
(DPP) and reinforcement learning (RL) and propose a novel reinforcing DPP
(R-DPP) approach to generate a set of captions with high quality and diversity
for an image. We show that R-DPP performs better on accuracy and diversity than
using noise as a control signal (GANs, VAEs). Moreover, R-DPP is able to
preserve the modes of the learned distribution. Hence, beam search algorithm
can be applied to generate a single accurate caption, which performs better
than other RL-based models.

28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph