Summary of Vision-language Models Under Cultural and Inclusive Considerations, by Antonia Karamolegkou et al.
Vision-Language Models under Cultural and Inclusive Considerations
by Antonia Karamolegkou, Phillip Rust, Yong Cao, Ruixiang Cui, Anders Søgaard, Daniel Hershcovich
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a culture-centric evaluation benchmark to assess the reliability of large vision-language models (VLMs) in describing images for visually impaired individuals from diverse cultural backgrounds. To develop this benchmark, the authors conducted a survey to determine caption preferences and filtered the existing VizWiz dataset with images taken by people who are blind. The results show promising performance for state-of-the-art models, but also highlight challenges such as hallucination and misalignment of automatic evaluation metrics with human judgment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps visually impaired people by testing large computer models that describe pictures. These models might be helpful tools for people who can’t see to understand what’s in their daily life photos. The problem is that the current tests used to evaluate these models don’t include diverse cultural backgrounds or the situations where someone would use this technology. To fix this, the authors asked people with visual impairments how they prefer captions and created a new test dataset using images taken by blind individuals. They then tested several top computer models to see if they can be trusted in real-life situations. While some of these models did well, the authors also found some problems that need to be fixed before this technology can be widely used. |
Keywords
» Artificial intelligence » Hallucination
