Improving Image Captioning with Better Use of Captions
This work addresses the multimodal challenge of image captioning for applications in AI and vision-language tasks, representing an incremental improvement over existing methods.
The paper tackled the problem of generating better captions for images by leveraging caption semantics to improve both image representation and caption generation, achieving state-of-the-art performance on the MSCOCO dataset across multiple metrics.
Image captioning is a multimodal problem that has drawn extensive attention in both the natural language processing and computer vision community. In this paper, we present a novel image captioning architecture to better explore semantics available in captions and leverage that to enhance both image representation and caption generation. Our models first construct caption-guided visual relationship graphs that introduce beneficial inductive bias using weakly supervised multi-instance learning. The representation is then enhanced with neighbouring and contextual nodes with their textual and visual features. During generation, the model further incorporates visual relationships using multi-task learning for jointly predicting word and object/predicate tag sequences. We perform extensive experiments on the MSCOCO dataset, showing that the proposed framework significantly outperforms the baselines, resulting in the state-of-the-art performance under a wide range of evaluation metrics.