Dynamic-balanced double-attention fusion for image captioning

Image captioning has received significant attention in the cross-modal field in which spatial and channel attentions play a crucial role. However, such attention-based approaches ignore two issues: (1) errors or noise in the channel feature map amplifies in the spatial feature map, leading to a lower model reliability; (2) image spatial feature and channel feature provide different contributions to the prediction both function words (e.g., "in'', "out'' and "on'') and notional words (e.g., "girl'', "teddy'' and "bear''). To alleviate the above issues, in this paper we propose the Dynamic-Balanced Double-Attention Fusion (DBDAF) for image captioning task that novelly exploits the attention variation and enhances the overall performance of the model. Technically, DBDAF first integrates a parallel Double Attention Network (DAN) in which channel attention is capitalized on as a supplement to the region attention, enhancing the model reliability. Then, a attention variation based Balancing Attention Fusion Mechanism (BAFM) module is devised. When predicting function words and notional words, BAFM makes a dynamic balance between channel attention and region attention based on attention variation. Moreover, to achieve the richer image description, we further devise a Doubly Stochastic Regularization (DSR) penalty and integrate it into the model loss function. Such DSR makes the model equally focus on every pixel and every channel in generating entire sentence. Extensive experiments on the three typical datasets show our DBDAF outperforms the related end-to-end leading approaches clearly. More remarkably, DBDAF achieves 1.04% and 1.75% improvement in terms of BLEU4 and CIDEr on the MSCOCO datasets.