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Exploiting relationships among objects has achieved remarkable progress ininterpreting images or videos by natural language. Most existing methods resortto first detecting objects and their relationships, and then generating textualdescriptions, which heavily depends on pre-trained detectors and leads toperformance drop when facing problems of heavy occlusion, tiny-size objects andlong-tail in object detection. In addition, the separate procedure of detectingand captioning results in semantic inconsistency between the pre-definedobject/relation categories and the target lexical words. We exploit prior humancommonsense knowledge for reasoning relationships between objects without anypre-trained detectors and reaching semantic coherency within one image or videoin captioning. The prior knowledge (e.g., in the form of knowledge graph)provides commonsense semantic correlation and constraint between objects thatare not explicit in the image and video, serving as useful guidance to buildsemantic graph for sentence generation. Particularly, we present a jointreasoning method that incorporates 1) commonsense reasoning for embedding imageor video regions into semantic space to build semantic graph and 2) relationalreasoning for encoding semantic graph to generate sentences. Extensiveexperiments on the MS-COCO image captioning benchmark and the MSVD videocaptioning benchmark validate the superiority of our method on leveraging priorcommonsense knowledge to enhance relational reasoning for visual captioning.

Relational Reasoning using Prior Knowledge for Visual Captioning