  Although traditionally used in the machine translation field, the
encoder-decoder framework has been recently applied for the generation of video
and image descriptions. The combination of Convolutional and Recurrent Neural
Networks in these models has proven to outperform the previous state of the
art, obtaining more accurate video descriptions. In this work we propose
pushing further this model by introducing two contributions into the encoding
stage. First, producing richer image representations by combining object and
location information from Convolutional Neural Networks and second, introducing
Bidirectional Recurrent Neural Networks for capturing both forward and backward
temporal relationships in the input frames.
