Context Matters for Image Descriptions for Accessibility: Challenges for Referenceless Evaluation Metrics

Kreiss, Elisa; Bennett, Cynthia L.; Hooshmand, Shayan; Zelikman, Eric; Morris, Meredith Ringel; Potts, Christopher

doi:10.18653/v1/2022.emnlp-main.309

Our system is currently under heavy load due to increased usage. We're actively working on upgrades to improve performance. Thank you for your patience.

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing 2022

DOI: 10.18653/v1/2022.emnlp-main.309

|View full text |Cite

Context Matters for Image Descriptions for Accessibility: Challenges for Referenceless Evaluation Metrics

Elisa Kreiss¹,

Cynthia L. Bennett²,

Shayan Hooshmand³

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Other4

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Context-VQA: Towards Context-Aware and Purposeful Visual Question Answering

Naik,

Potts,

Kreiss

2023

2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

View full text Add to dashboard Cite

Context-VQA: Towards Context-Aware and Purposeful Visual Question Answering

Naik,

Potts,

Kreiss

2023

2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

View full text Add to dashboard Cite

The Potential of a Visual Dialogue Agent In a Tandem Automated Audio Description System for Videos

Stangl,

Ihorn,

Siu

et al. 2023

The 25th International ACM SIGACCESS Conference on Computers and Accessibility

View full text Add to dashboard Cite

Opportunities for Accessible Virtual Reality Design for Immersive Musical Performances for Blind and Low-Vision People

Dang,

Korreshi,

Iqbal

et al. 2023

Proceedings of the 2023 ACM Symposium on Spatial User Interaction

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Context Matters for Image Descriptions for Accessibility: Challenges for Referenceless Evaluation Metrics

Cited by 4 publications

References 0 publications

Context-VQA: Towards Context-Aware and Purposeful Visual Question Answering

Context-VQA: Towards Context-Aware and Purposeful Visual Question Answering

The Potential of a Visual Dialogue Agent In a Tandem Automated Audio Description System for Videos

Opportunities for Accessible Virtual Reality Design for Immersive Musical Performances for Blind and Low-Vision People

Contact Info

Product

Resources

About