# Boost Image Captioning with Knowledge Reasoning

Source: [Boost Image Captioning with Knowledge Reasoning](<https://www.sota2.com/research/paper/68fa68aa1e209131cea1d94a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:42:59.203Z

Authors: Feicheng Huang, Zhixin Li, Haiyang Wei, Canlong Zhang, Huifang Ma

Published: 2020-11-02T12:19:46.000Z

## Abstract

Automatically generating a human-like description for a given image is a potential research in artificial intelligence, which has attracted a great of attention recently. Most of the existing attention methods explore the mapping relationships between words in sentence and regions in image, such unpredictable matching manner sometimes causes inharmonious alignments that may reduce the quality of generated captions. In this paper, we make our efforts to reason about more accurate and meaningful captions. We first propose word attention to improve the correctness of visual attention when generating sequential descriptions word-by-word. The special word attention emphasizes on word importance when focusing on different regions of the input image, and makes full use of the internal annotation knowledge to assist the calculation of visual attention. Then, in order to reveal those incomprehensible intentions that cannot be expressed straightforwardly by machines, we introduce a new strategy to inject external knowledge extracted from knowledge graph into the encoder-decoder framework to facilitate meaningful captioning. Finally, we validate our model on two freely available captioning benchmarks: Microsoft COCO dataset and Flickr30k dataset. The results demonstrate that our approach achieves state-of-the-art performance and outperforms many of the existing approaches.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2011.00927>)

[PDF](<https://arxiv.org/pdf/2011.00927.pdf>)