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 vision-text… See the full description on the dataset page:
https://huggingface.co/datasets/jinyoungkim/NExT-GQA.
