jinyoungkim/NExT-GQA
Can I Trust Your Answer? Visually Grounded Video Question Answering Introduction We study visually grounded VideoQA by forcing vision-language models (VLMs) to answer questions and simultaneously ground the relevant video moments as visual evidences. We show that this task is easy for human yet is extremely challenging for existing VLMs, revealing that the strong QA performance of these models may largely due to short-cut learning (e.g., language priors and spurious… See the full description on the dataset page: https://huggingface.co/datasets/jinyoungkim/NExT-GQA.
| Author / organization | jinyoungkim |
|---|---|
| Downloads | 6.3K (as reported by Hugging Face) |
| Likes | 2 |
| Created | 23 April 2025 |
| Last updated at source | 23 April 2025 |
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- 16 Oct 2026 Description: Can I Trust Your Answer? Visually Grounded Video Question Answering Introduction We study visually grounded VideoQA by forcing vision-language models (VLMs) to answer questions and simultaneously ground the relevant video moments as visual → Can I Trust Your Answer? Visually Grounded Video Question Answering Introduction We study visually grounded VideoQA by forcing vision-language models (VLMs) to answer questions and simultaneously ground the relevant video moments as visual
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