Omkar2118/datasets huggingface dataset:
IntentQA

We contribute an IntentQA dataset with diverse intents in daily social activities.

We utilize NExT-QA as the source dataset to construct our dataset. NExT-QA dataset is a comprehensive VideoQA dataset with rich natural daily social activities and detailed QA annotations. Originally, the NExT-QA dataset categorizes itself into three types, i.e., Causal, Temporal, Descriptive. We select the inference QA types, i.e., Causal and Temporal, rather than the factoid Descriptive, to build our IntentQA dataset. Particularly, we select both the Causal Why and Causal How subtypes under Causal, and the Temporal Previous and Temporal Next subtypes under Temporal. The Causal Why (CW) QA usually takes the form of ‘Why [action]? For [intent]’, with the key action appearing in the question and the intent in the answer. On the contrary, the Causal How (CH) QA usually takes the form of ‘How [intent]? By [action]’, with the key action appearing in the answer and the intent in the question. The Temporal Previous (TP) QA usually takes the form of ‘What [action A] before [action B]? ’, while the Temporal Next (TN) QA takes the form of ‘What [action B] after [action A]? ’. In the TP&TN QA, the intent is not explicitly expressed in the question nor answer, but is the implicit causal factor linking the two sequential actions.

Introducing paper: IntentQA: Context-aware Video Intent Reasoning
NExT-GQA

We study visually grounded VideoQA in response to the emerging trends of utilizing pretraining techniques for video-language understanding. Specifically, by forcing vision-language models (VLMs) to answer questions and simultaneously provide visual evidence, we seek to ascertain the extent to which the predictions of such techniques are genuinely anchored in relevant video content, versus spurious correlations from language or irrelevant visual context. Towards this, we construct NExT-GQA -- an extension of NExT-QA with 10.5K temporal grounding (or location) labels tied to the original QA pairs. With NExT-GQA, we scrutinize a variety of state-of-the-art VLMs. Through post-hoc attention analysis, we find that these models are weak in substantiating the answers despite their strong QA performance. This exposes a severe limitation of these models in making reliable predictions.

Introducing paper: Can I Trust Your Answer? Visually Grounded Video Question Answering