  In recent years, the biggest advances in major Computer Vision tasks, such as
object recognition, handwritten-digit identification, facial recognition, and
many others., have all come through the use of Convolutional Neural Networks
(CNNs). Similarly, in the domain of Natural Language Processing, Recurrent
Neural Networks (RNNs), and Long Short Term Memory networks (LSTMs) in
particular, have been crucial to some of the biggest breakthroughs in
performance for tasks such as machine translation, part-of-speech tagging,
sentiment analysis, and many others. These individual advances have greatly
benefited tasks even at the intersection of NLP and Computer Vision, and
inspired by this success, we studied some existing neural image captioning
models that have proven to work well. In this work, we study some existing
captioning models that provide near state-of-the-art performances, and try to
enhance one such model. We also present a simple image captioning model that
makes use of a CNN, an LSTM, and the beam search1 algorithm, and study its
performance based on various qualitative and quantitative metrics.
