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In this work we focus on the problem of image caption generation. We proposean extension of the long short term memory (LSTM) model, which we coin gLSTMfor short. In particular, we add semantic information extracted from the imageas extra input to each unit of the LSTM block, with the aim of guiding themodel towards solutions that are more tightly coupled to the image content.Additionally, we explore different length normalization strategies for beamsearch in order to prevent from favoring short sentences. On various benchmarkdatasets such as Flickr8K, Flickr30K and MS COCO, we obtain results that are onpar with or even outperform the current state-of-the-art.

Guiding Long-Short Term Memory for Image Caption Generation