Granularity-aware and Semantic Aggregation Based Image-Text Retrieval Network

Image-text retrieval is a basic task in visual-language domain,which aims at mining the relationships between different modalities.However,most existing approaches rely heavily on associating specific regions of an image with each word in a sentence with similar semantics and underappreciate the significance of multi-granular information in images,resulting in irrelevant matches between the two modalities and semantically ambiguous embedding.Generally,an image contains object-level,action-le-vel,relationship-level or even scene-level information that is not explicitly labeled.Therefore,it is challenging to align complex visual information with ambiguous descriptions.To tackle this issue,this paper proposes a granularity aware and semantic aggregating(GASA) network to obtain multi-visual representations and narrow the cross-modal gap.Specifically,the granularity-aware feature selection module selects copious multi-granularity information of images and conducts a multi-scale fusion,guided by an adaptive gated fusion mechanism and a pyramid structure.The semantic aggregation module clusters the multi-granularity information from visual and textual clues in a shared space to obtain the residual representations.Experiments are conducted on two benchmark datasets,and the results show our model outperforms the state-of-the-arts by over 2% on R@1 of MSCOCO 1k.Besides,our model outperforms the state-of-the-art by 4.1% in terms of Flickr30k on R@Sum.