  The task of multi-image cued story generation, such as visual storytelling
dataset (VIST) challenge, is to compose multiple coherent sentences from a
given sequence of images. The main difficulty is how to generate image-specific
sentences within the context of overall images. Here we propose a deep learning
network model, GLAC Net, that generates visual stories by combining
global-local (glocal) attention and context cascading mechanisms. The model
incorporates two levels of attention, i.e., overall encoding level and image
feature level, to construct image-dependent sentences. While standard attention
configuration needs a large number of parameters, the GLAC Net implements them
in a very simple way via hard connections from the outputs of encoders or image
features onto the sentence generators. The coherency of the generated story is
further improved by conveying (cascading) the information of the previous
sentence to the next sentence serially. We evaluate the performance of the GLAC
Net on the visual storytelling dataset (VIST) and achieve very competitive
results compared to the state-of-the-art techniques. Our code and pre-trained
models are available here.
