Recent studies have shown promising results on utilizing large pre-trained image-language models for video question answering.

Reasoning over TKGs has two challenges: 1.

Despite this remarkable progress, there is a noticeable absence of datasets that combine electrocardiogram (ECG) data with question answering.

Visual question answering ( VQA) is a challenging task that requires an in-depth understanding of vision and language, as well as multi-modal reasoning.

Why was the dataset created? Has the dataset been used already? QA dataset has already been used. QA establishes a framework to benchmark question answering at the present time: answers (e.g., the number of Shohei Ohtani's home runs) change in real time. This could also include the system's interactions with its information retrieval module (for How many instances are there?

We build strong baseline models upon large pretrained language models, including GPT -3 and T5. Our benchmark is an ongoing effort, and this paper presents real-time evaluation results over the past year.

As mentioned in Section 4.2 Our model can easily adapt to various video backbones. We use QA accuracy as the metric for evaluation. As illustrated in Section 3.2, with event-level annotations, we First, we analyze the effects of different loss functions on model performance. The results are shown in Figure 1. When the coefficient of any loss function is 0, the performance of the model decreases, which indicates their efficiency in event memory extraction. Without it, there is a significant decrease in model performance.

Video Question Answering (VideoQA) has emerged as a vital tool to evaluate agents' ability to understand human daily behaviors. Despite the recent success of large vision language models in many multi-modal tasks, complex situation reasoning over videos involving multiple human-object interaction events still remains challenging.