Attention-guided image captioning with adaptive global and local feature fusion

Although attention mechanisms are exploited widely in encoder-decoder neural network-based image captioning framework, the relation between the selection of salient image regions and the supervision of spatial information on local and global representation learning was overlooked, thereby degrading captioning performance. Consequently, we propose an image captioning scheme based on adaptive spatial information attention (ASIA), extracting a sequence of spatial information of salient objects in a local image region or an entire image. Specifically, in the encoding stage, we extract the object-level visual features of salient objects and their spatial bounding-box. We obtain the global feature maps of an entire image, which are fused with local features and the fused features are fed into the LSTM-based language decoder. In the decoding stage, our adaptive attention mechanism dynamically selects the corresponding image regions specified by an image description. Extensive experiments conducted on two datasets demonstrate the effectiveness of the proposed method.

Automated summary

The proposed image captioning scheme based on adaptive spatial information attention (ASIA) effectively extracts spatial information of salient objects, utilizes different techniques in encoding and decoding stages, improving captioning performance according to extensive experiments on two datasets.