Weakly-Supervised Video Object Grounding by Exploring Spatio-Temporal Contexts
Xun Yang, Xueliang liu, Meng Jian, Xinjian Gao, Meng Wang
Abstract
Grounding objects in visual context from natural language queries is a crucial yet challenging vision-and-language task, which has gained increasing attention in recent years. Existing work has primarily investigated this task in the context of still images. Despite their effectiveness, these methods cannot be directly migrated into the video context, mainly due to 1) the complex spatio-temporal structure of videos and 2) the scarcity of fine-grained annotations of videos. To effectively ground objects in videos is profoundly more challenging and less explored.
Topics & Concepts
Computer scienceTask (project management)Context (archaeology)Object (grammar)Artificial intelligenceGroundHuman–computer interactionTask analysisNatural languageNatural (archaeology)Common groundComputer visionNatural language processingCommunicationGeographyPsychologyEngineeringSystems engineeringElectrical engineeringArchaeologyMultimodal Machine Learning ApplicationsHuman Pose and Action RecognitionDomain Adaptation and Few-Shot Learning