Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the use of human reference data for evaluating automatic image
  descriptions   Automatic image description systems are commonly trained and evaluated using
crowdsourced, human-generated image descriptions. The best-performing system is
then determined using some measure of similarity to the reference data (BLEU,
Meteor, CIDER, etc). Thus, both the quality of the systems as well as the
quality of the evaluation depends on the quality of the descriptions. As
Section 2 will show, the quality of current image description datasets is
insufficient. I argue that there is a need for more detailed guidelines that
take into account the needs of visually impaired users, but also the
feasibility of generating suitable descriptions. With high-quality data,
evaluation of image description systems could use reference descriptions, but
we should also look for alternatives.

26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
3
The categories of this paper are: physics
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph