  Image Captioning, or the automatic generation of descriptions for images, is
one of the core problems in Computer Vision and has seen considerable progress
using Deep Learning Techniques. We propose to use Inception-ResNet
Convolutional Neural Network as encoder to extract features from images,
Hierarchical Context based Word Embeddings for word representations and a Deep
Stacked Long Short Term Memory network as decoder, in addition to using Image
Data Augmentation to avoid over-fitting. For data Augmentation, we use
Horizontal and Vertical Flipping in addition to Perspective Transformations on
the images. We evaluate our proposed methods with two image captioning
frameworks- Encoder-Decoder and Soft Attention. Evaluation on widely used
metrics have shown that our approach leads to considerable improvement in model
performance.
