Papers by Kyle Seelman
What’s Different between Visual Question Answering for Machine “Understanding” Versus for Accessibility? (2022.aacl-main)
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| Challenge: | Existing benchmarking datasets for visual question answering focus on machine "understanding" but it remains unclear how progress on those datasets corresponds to improvements in this real-world use case. |
| Approach: | They evaluate the visual question answering task by evaluating a variety of VQA models. |
| Outcome: | The proposed model can achieve high scores on tasks thought to require human-like comprehension, including image tagging and captioning. |