Temporal sentence grounding in videos (TSGV), aka, natural language video localization
(NLVL) or video moment retrieval (VMR), aims to retrieve a temporal moment that …

Recently, visual question answering (VQA) has gained considerable interest within the
computer vision and natural language processing (NLP) research areas. The VQA task …

Relation extraction (RE) is a fundamental process in constructing knowledge graphs.
However, previous methods on relation extraction suffer sharp performance decline in short …

Grounding language queries in videos aims at identifying the time interval (or moment)
semantically relevant to a language query. The solution to this challenging task demands …

Graph convolutional networks have been successfully applied to aspect-based sentiment
analysis, due to their ability to flexibly capture syntactic information and word dependencies …

Responsing with image has been recognized as an important capability for an intelligent
conversational agent. Yet existing works only focus on exploring the multimodal dialogue …

Most existing temporal action localization (TAL) methods rely on a transfer learning pipeline:
by first optimizing a video encoder on a large action classification dataset (ie, source …

Text-based visual question answering (TextVQA) faces the significant challenge of avoiding
redundant relational inference. To be specific, a large number of detected objects and …

Abstract The Visual Question Answering (VQA) task combines challenges for processing
data with both Visual and Linguistic processing, to answer basic 'common sense'questions …

The task of image difference captioning aims at locating changed objects in similar image
pairs and describing the difference with natural language. The key challenges of this task …