Integrating Image Captioning with Rule-based Entity Masking

22 Jul 2020  ·  Aditya Mogadala, Xiaoyu Shen, Dietrich Klakow ·

Given an image, generating its natural language description (i.e., caption) is a well studied problem. Approaches proposed to address this problem usually rely on image features that are difficult to interpret. Particularly, these image features are subdivided into global and local features, where global features are extracted from the global representation of the image, while local features are extracted from the objects detected locally in an image. Although, local features extract rich visual information from the image, existing models generate captions in a blackbox manner and humans have difficulty interpreting which local objects the caption is aimed to represent. Hence in this paper, we propose a novel framework for the image captioning with an explicit object (e.g., knowledge graph entity) selection process while still maintaining its end-to-end training ability. The model first explicitly selects which local entities to include in the caption according to a human-interpretable mask, then generate proper captions by attending to selected entities. Experiments conducted on the MSCOCO dataset demonstrate that our method achieves good performance in terms of the caption quality and diversity with a more interpretable generating process than previous counterparts.

PDF Abstract
No code implementations yet. Submit your code now


  Add Datasets introduced or used in this paper

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here