Video understanding has achieved great success in representation learning, such as video
caption, video object grounding, and video descriptive question-answer. However, current …

We introduce NExT-QA, a rigorously designed video question answering (VideoQA)
benchmark to advance video understanding from describing to explaining the temporal …

Abstract Video Question Answering (VideoQA) is challenging as it requires capturing
accurate correlations between modalities from redundant information. Recent methods focus …

This paper proposes a Video Graph Transformer (VGT) model for Video Question Answering
(VideoQA). VGT's uniqueness are two-fold: 1) it designs a dynamic graph transformer …

In this paper, we propose a novel task IntentQA, a special VideoQA task focusing on video
intent reasoning, which has become increasingly important for AI with its advantages in …

Abstract Video Question Answering (VideoQA) is the task of answering questions about a
video. At its core is understanding the alignments between visual scenes in video and …

We present the task of Spatio-Temporal Video Question Answering, which requires
intelligent systems to simultaneously retrieve relevant moments and detect referenced visual …

This paper strives to solve complex video question answering (VideoQA) which features
long videos containing multiple objects and events at different time. To tackle the challenge …

Video question answering is a challenging task, which requires agents to be able to
understand rich video contents and perform spatial-temporal reasoning. However, existing …

Existing methods for video question answering (VideoQA) often suffer from spurious
correlations between different modalities, leading to a failure in identifying the dominant …