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 …

Video Question Answering (VideoQA) aims to answer natural language questions according
to the given videos. It has earned increasing attention with recent research trends in joint …

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 …

We propose to perform video question answering (VideoQA) in a Co ntrastive manner via a
V ideo G raph T ransformer model (CoVGT). CoVGT's uniqueness and superiority are three …

Most existing image-text matching methods adopt triplet loss as the optimization objective,
and choosing a proper negative sample for the triplet of< anchor, positive, negative> is …

Video Question Answering (VideoQA) is the task of answering the natural language
questions about a video. Producing an answer requires understanding the interplay across …

Video question answering (VideoQA) is a challenging yet important task that requires a joint
understanding of low-level video content and high-level textual semantics. Despite the …

Answering questions about complex situations in videos requires not only capturing of the
presence of actors, objects, and their relations, but also the evolution of these relationships …

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

Most existing cross-modal retrieval methods employ two-stream encoders with different
architectures for images and texts, eg, CNN for images and RNN/Transformer for texts. Such …