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 …

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 …

Relational reasoning is at the heart of video question answering. However, existing
approaches suffer from several common limitations:(1) they only focus on either object-level …

Abstract Video Question Answering (Video QA) challenges modelers in multiple fronts.
Modeling video necessitates building not only spatio-temporal models for the dynamic visual …

Video visual relation recognition aims at mining the dynamic relation instances between
objects in the form of< subject, predicate, object>, such as “person1-towards-person2” and …

Video question answering (Video QA) presents a powerful testbed for human-like intelligent
behaviors. The task demands new capabilities to integrate video processing, language …

Video question answering (VideoQA) has witnessed remarkable progress in the past few
years, but there are still challenges in precisely locating question-related segments and …

This paper presents a new video question answering task on screencast tutorials. We
introduce a dataset including question, answer and context triples from the tutorial videos for …

Consider the scenario where a human cleans a table and a robot observing the scene is
instructed with the task" Remove the cloth using which I wiped the table". Instruction …

Visual perception and language understanding are-fundamental components of human
intelligence, enabling them to understand and reason about objects and their interactions. It …