Distilled Cross-Combination Transformer for Image Captioning with Dual Refined Visual Features

Transformer-based encoders that encode both region and grid features are the preferred choice for the image captioning task due to their multi-head self-attention mechanism. This mechanism ensures superior capture of relationships and contextual information between various regions in an image. However, because of the Transformer block stacking, self-attention computes the visual features several times, increasing computing costs and producing a great deal of redundant feature calculation. In this paper, we propose a novel Distilled Cross-Combination Transformer (DCCT) network. Specifically, we first design a distillation cascade fusion encoder(DCFE) to filter out redundant features in visual features that affect attentional focus, obtaining refined features. Additionally, we introduce a parallel cross-fusion attention module (PCFA) that fully utilizes the complementarity and correlation between grid and region features to better fuse the encoded dual visual features. Extensive experiments on the MSCOCO dataset demonstrate that the proposed DCCT strategy outperforms many state-of-the-art techniques and attains exceptional performance.