  This paper describes our winning entry in the ImageCLEF 2015 image sentence
generation task. We improve Google's CNN-LSTM model by introducing
concept-based sentence reranking, a data-driven approach which exploits the
large amounts of concept-level annotations on Flickr. Different from previous
usage of concept detection that is tailored to specific image captioning
models, the propose approach reranks predicted sentences in terms of their
matches with detected concepts, essentially treating the underlying model as a
black box. This property makes the approach applicable to a number of existing
solutions. We also experiment with fine tuning on the deep language model,
which improves the performance further. Scoring METEOR of 0.1875 on the
ImageCLEF 2015 test set, our system outperforms the runner-up (METEOR of
0.1687) with a clear margin.
