  It is always well believed that modeling relationships between objects would
be helpful for representing and eventually describing an image. Nevertheless,
there has not been evidence in support of the idea on image description
generation. In this paper, we introduce a new design to explore the connections
between objects for image captioning under the umbrella of attention-based
encoder-decoder framework. Specifically, we present Graph Convolutional
Networks plus Long Short-Term Memory (dubbed as GCN-LSTM) architecture that
novelly integrates both semantic and spatial object relationships into image
encoder. Technically, we build graphs over the detected objects in an image
based on their spatial and semantic connections. The representations of each
region proposed on objects are then refined by leveraging graph structure
through GCN. With the learnt region-level features, our GCN-LSTM capitalizes on
LSTM-based captioning framework with attention mechanism for sentence
generation. Extensive experiments are conducted on COCO image captioning
dataset, and superior results are reported when comparing to state-of-the-art
approaches. More remarkably, GCN-LSTM increases CIDEr-D performance from 120.1%
to 128.7% on COCO testing set.
