A transformer-based deep learning model for evaluation of accessibility of image descriptions

Author:

Shrestha Raju1

Affiliation:

1. Department of Computer Science, Oslo Metropolitan University (OsloMet), Norway

Publisher

ACM

Reference28 articles.

1. Peter Anderson , Basura Fernando , Mark Johnson , and Stephen Gould . 2016 . SPICE: Semantic Propositional Image Caption Evaluation. In Computer Vision – ECCV 2016, Bastian Leibe, Jiri Matas , Nicu Sebe, and Max Welling (Eds.). Springer International Publishing , Cham, 382–398. Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. 2016. SPICE: Semantic Propositional Image Caption Evaluation. In Computer Vision – ECCV 2016, Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling (Eds.). Springer International Publishing, Cham, 382–398.

2. Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering

3. Satanjeev Banerjee and Alon Lavie . 2005 . METEOR: An Automatic Metric for MT Evaluation withImproved Correlation with Human Judgments . In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translationand/or Summarization. Association for Computational Linguistics (ACL), 65–72 . Satanjeev Banerjee and Alon Lavie. 2005. METEOR: An Automatic Metric for MT Evaluation withImproved Correlation with Human Judgments. In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translationand/or Summarization. Association for Computational Linguistics (ACL), 65–72.

4. A study on web experience among visually impaired users in Malaysia

5. Increasing web accessibility by automatically judging alternative text quality

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