metrics. In particular SentiCap has the highest number of success in placing at least one sentiment word into the caption, 88% positive (or 72% negative) captions are perceived by crowd workers as more positive (or negative) than the factual caption, with a similar descriptiveness rating. ]{}

Related Work
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[Recent]{} advances in visual recognition have made “an image is a thousand words” much closer to reality, [largely due to the advances in Convolutional Neural Networks (CNN) [@Simonyan2015; @Szegedy_2015_CVPR]]{}. [A related topic also advancing rapidly is image captioning, where most early systems were]{} [based on similarity retrieval using objects and attributes [@farhadi2010every; @kulkarni2011baby; @hodosh2013framing; @Gupta2012a], and assembling sentence fragments such as object-action-scene [@farhadi2010every], subject-verb-object [@rohrbach2013translating], object-attribute-prepositions [@kulkarni2011baby] or global image properties such as scene and lighting [@Nwogu2011].]{} [Recent systems model richer language structure, such as formulating a integer linear program to map visual elements to the parse tree of a sentence [@kuznetsova2014treetalk], or embedding  [@Xu2015] video and compositional semantics into a joint space.]{}

Word-level language models [such as RNNs [@mikolov2011strategies; @sutskever2011generating] and maximum-entropy (max-ent) language models [@mikolov2011strategies] have improved with the aid of]{} significantly larger datasets and more computing power. Several research teams independently proposed image captioning systems that combine CNN-based 