  Recently, Deep Learning (DL) methods have shown an excellent performance in
image captioning and visual question answering. However, despite their
performance, DL methods do not learn the semantics of the words that are being
used to describe a scene, making it difficult to spot incorrect words used in
captions or to interchange words that have similar meanings. This work proposes
a combination of DL methods for object detection and natural language
processing to validate image's captions. We test our method in the FOIL-COCO
data set, since it provides correct and incorrect captions for various images
using only objects represented in the MS-COCO image data set. Results show that
our method has a good overall performance, in some cases similar to the human
performance.
