Alongside huge volumes of research on deep learning models in NLP in the recent years,
there has been much work on benchmark datasets needed to track modeling progress …

With the increasing interest in the content creation field in multiple sectors such as media,
education, and entertainment, there is an increased trend in the papers that use AI …

Video question answering (VideoQA) is a complex task that requires diverse multi-modal
data for training. Manual annotation of question and answers for videos, however, is tedious …

Recent methods for visual question answering rely on large-scale annotated datasets.
Manual annotation of questions and answers for videos, however, is tedious, expensive and …

With the burgeoning growth of online video platforms and the escalating volume of video
content, the demand for proficient video understanding tools has intensified markedly. Given …

Recent years have witnessed an increasing interest in image-based question-answering
(QA) tasks. However, due to data limitations, there has been much less work on video-based …

Video question answering (VideoQA) is challenging as it requires modeling capacity to distill
dynamic visual artifacts and distant relations and to associate them with linguistic concepts …

Recent developments in modeling language and vision have been successfully applied to
image question answering. It is both crucial and natural to extend this research direction to …

Recent text-to-video (T2V) generation methods have seen significant advancements.
However, the majority of these works focus on producing short video clips of a single event …

We introduce TV show Retrieval (TVR), a new multimodal retrieval dataset. TVR requires
systems to understand both videos and their associated subtitle (dialogue) texts, making it …